S1090: AI in Agroecosystems: Big Data and Smart Technology-Driven Sustainable Production

(Multistate Research Project)

Status: Active

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SAES-422 Reports

08/31/2022

Alabama: Aubrun University 


Citation of the conference proceedings paper: Oliveira, M.F., F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco. 2022. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target Regression. In Proceedings of the 2022 International Conference of Precision Agriculture. June 26-29, 2022 Minneapolis.


 


Florida: University of Florida


Yuan, W., Choi, D., Bolkas, D., Heinemann, P.H. and He, L., 2022. Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification. International Journal of Remote Sensing, 43(8), pp.3106-3130.


Yuan, W., Choi, D., & Bolkas, D. (2022). GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees. Computers and Electronics in Agriculture, 197, 106966.


Zahid, A., Mahmud, M.S., He, L., Schupp, J., Choi, D. and Heinemann, P., 2022. An Apple Tree Branch Pruning Analysis. HortTechnology, 32(2), pp.90-98.


Zhang, H., He, L., Di Gioia, F., Choi, D., Elia, A. and Heinemann, P., 2022. LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato. Smart Agricultural Technology, 2, p.100053.


Mahmud, M.S., Zahid, A., He, L., Choi, D., Krawczyk, G. and Zhu, H., 2021. LiDAR-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers. Computers and Electronics in Agriculture, 191, p.106565.


Patel, A., W. S. Lee, N. A. Peres, and C. W. Fraisse. 2021. Strawberry plant wetness detection using computer vision and deep learning. Smart Agricultural Technology 1, 2021, 100013, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.100013


Yun, C., H.-J. Kim, C.-W. Jeon, M. Gang, W. S. Lee, and J. G. Han. 2021. Stereovision-based ridge-furrow detection and tracking for auto-guided cultivator. Computers and Electronics in Agriculture 191, 2021, 106490, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2021.106490.


Puranik, P., W. S. Lee, N. Peres, F. Wu, A. Abd-Elrahman, and S. Agehara. 2021. Strawberry flower and fruit detection using deep learning for developing yield prediction models. In the Proceedings of the 13th European Conference on Precision Agriculture (ECPA), July 19-22, 2021, Budapest, Hungary.


Zhou, X., W. S. Lee, Y. Ampatzidis, Y. Chen, N. Peres, and C. Fraisse. 2021. Strawberry maturity classification from UAV and near-ground imaging using deep learning. Smart Agricultural Technology 1, 2021, 100001, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.10000


Costa L., McBreen J., Ampatzidis Y., Guo J., Reisi Gahrooei M., Babar A., 2021. Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes. Precision Agriculture, 23 (2), 622-642.


Costa L., Ampatzidis Y., Rohla C., Maness N., Cheary B., Zhang L., 2021. Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Computers and Electronics in Agriculture, 181, 105964, doi.org/10.1016/j.compag.2020.105964.


Costa L., Archer L., Ampatzidis Y., Casteluci L., Caurin G.A.P., Albrecht U., 2021. Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence. Precision Agriculture 22, 1107-1119, https://doi.org/10.1007/s11119-020-09771-x.


Nunes L., Ampatzidis Y., Costa L., Wallau M., 2021. Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Computers and Electronics in Agriculture, 183, 106080, doi.org/10.1016/j.compag.2021.106080.


Vijayakumar V., Costa L., Ampatzidis Y., 2021. Prediction of citrus yield with AI using ground-based fruit detection and UAV imagery. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, 2100493, doi:10.13031/aim.202100493.


Zhou, C., W. S. Lee, O. E. Liburd, I. Aygun, J. K. Schueller, and I. Ampatzidis. 2021. Smartphone-based tool for two-spotted spider mite detection in strawberry. ASABE Paper No. 2100558. St. Joseph, MI.: ASABE.


Zhou, X., Y. Ampatzidis, W. S. Lee, and S. Agehara. 2021. Postharvest strawberry bruise detection using deep learning. ASABE Paper No. 2100458. St. Joseph, MI.: ASABE.


Influence of Planting Date, Maturity Group, and Harvest Timing on Soybean (Glycine max (L.) Yield and Seed Quality, PRISCILA CAMPOS, DONNIE MILLER, JOSH COPES, MELANIE NETTERVILLE, SEBE BROWN, TREY PRICE, DAVID MOSELEY, THANOS GENTIMIS, PETERS EGBEDI, RASEL PARVEJ3 (Accepted by Crop, Forage, & Turfgrass Management, Summer 2022). In this paper, modern methodologies were implemented in the analysis of the results, as well as more traditional statistical techniques.


The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle, BARBOSA JÚNIOR, M.R.; TEDESCO, D.; CARREIRA, V.S.; PINTO, A.A.; MOREIRA, B.R.A.; SHIRATSUCHI, L.S.; ZERBATO, C.; SILVA, R.P., Drones 2022, 6, 112. https://doi.org/10.3390/drones6050112


UAVs to Monitor and Manage Sugarcane: Integrative Review, BARBOSA JÚNIOR, M.R.; MOREIRA, B.R.A.; BRITO FILHO, A.L.; TEDESCO, D.; SHIRATSUCHI, L.S.; SILVA, R.P., Agronomy 2022, 12, 661. https://doi.org/10.3390/agronomy12030661


Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data, TEODORO, P. E.; TEODORO, L. P. R.; BAIO, F. H. R.; SILVA JUNIOR, C. A.; SANTOS, R. G.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; OSCO, L. P.; GONCALVES, W. N.; CARNEIRO, A. M.; MARCATO JUNIOR, J.; PISTORI, H.; SHIRATSUCHI, L. S., Remote Sensing, v. 13, p. 4632, 2021


Comparison of Machine Learning Techniques in Cotton Yield Prediction Using Satellite Remote Sensing, MORELLI-FERREIRA, F.; MAIA, N.J.C.; TEDESCO, D.; KAZAMA, E.H.; MORLIN CARNEIRO, F.; SANTOS, L.B.; SEBEN JUNIOR, G.F.; ROLIM, G.S.; SHIRATSUCHI, L.S.; SILVA, R.P. Preprints 2021, 2021120138 (doi: 10.20944/preprints202112.0138.v2). Published and in preparation for Remote Sensing.


 


 


Kentucky: University of Kentucky


Ekramirad, N., Khaled, Y.A., Doyle, L., Loeb, J., Donohue, K.D., Villanueva, R., and Adedeji, A.A. (2022). Nondestructive detection of codling moth infestation in apples using pixel-based NIR hyperspectral imaging with machine learning and feature selection. Foods 11(8), 1 - 16. (Citation: 2)


Rady, A., Watson, N., and Adedeji, A.A. (2021). Color imaging and machine learning for adulteration detection in minced meat. Journal of Agriculture and Food Research 6(100251), 1-11. (Citation: 1)


Watson, N.J., Bowler, A.L., Rady, A., Fisher, O.J., Simeone, A., Escrig, J., Woolley, E., and Adedeji, A.A. (2021). Intelligent sensors for sustainable food and drink manufacturing. Frontiers in Sustainable Food Systems. 5, 642786. (Citation: 5)


Ekramirad, N., Al Khaled, Y.A., Donohue, K., Villanueva, R., Parrish, C.A., and Adedeji, A. (2021). Development of pattern recognition and classification models for the detection of vibro-acoustic emissions from codling moth infested apples. Postharvest Biology and Technology 181, 111633. (Citation: 1)


Khaled, Y.A., Parrish, C. and *Adedeji, A.A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation. Comprehensive Reviews in Food Science and Food Safety. 20(4): 3438-3463. (Citation: 15)


 


Mississppi: Mississippi State University


Chen, D., Lu, Y., Li, Z., and Young, S.  2022.  Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems.  Computers and Electronics in Agriculture.


Yadav, P.K., Thomasson, J.A., Hardin, R.G., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Martin, D.E., Rodriguez III, R., Meza, K., Enciso, J. and Solorzano, J.  2022. Volunteer cotton plant detection in corn field with deep learning.  In Proc. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII (Vol. 12114, pp. 15-22). SPIE.


 


North Carolina: NC State  


Edward L. Kick, Laura McKinney, Steve McDonald, Andrew Jorgenson. “A Multiple-Network Analysis of the World System of Nations” is printed in Chinese and is used in what we hope will be a forthcoming publication in Sustainability.” That paper introduces the possibility of using artificial intelligence in farming around the world.


Edward L. Kick, Gregory Fulkerson, and Ahad Pezeshkpoor. “Agriculture Grains, and Beef Production: Remedies for food for Food Insecurity and the Ecological Footprint When the Cataclysm Comes?” Agricultural Research and Technology 23: 53-57.


Edward L. Kick “Cross-National Empirical Studies of Sustainability, Agriculture and the Environment: Cumulating Forward or Erring in an About Face?” Agricultural Research and Technology 25: 601-603.


Edward L. Kick. “Taking a World View”. College of Agriculture and Life Sciences Newsletter.


Edward L. Kick and Ahad Pezeshkpoor. “Biomes, World-System Positions, and National Characteristics as linked Precursors to Global Undernourishment and the Ecological Footprint” Under revision for publication in Sustainability.


 


 


South Carolina: Clemson University


Abenina MIA, Maja JM, Cutulle M, Melgar JC, Liu H. Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis.


AgriEngineering.2022;4(2):400-413. https://doi.org/10.3390/agriengineering4020027


 


Tennessee; University of Tennessee


Nasiri, A., Yoder, J., Zhao, Y., Hawkins, S., Prado, M., & Gan, H. (2022). Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Computers and Electronics in Agriculture, 197, 106931.


 


Texas: Texas A&M


Bhandari, M.; Baker, S.; Rudd, J. C.; Ibrahim, A. M. H.; Chang, A.; Xue, Q.; Jung, J.; Landivar, J.; Auvermann, B. Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (Uas)-Based Phenotyping. Remote Sens. 2021, 13 (6). https://doi.org/10.3390/rs13061144.


W. Wu, S. Hague, J. Jung, A. Ashapure, M. Maeda, A. Maeda, A. Chang, D. Jones, J.A. Thomasson, J. Landivar, "Cotton row spacing and Unmanned Aerial Vehicle sensors," Agronomy Journal, https://doi.org/10.1002/agj2.20902, 2021


A. Chang, J. Jung, J. Landivar, J. Landivar, B. Barker, R. Ghosh, "Performance evaluation of parallel structure from motion (SfM) processing with public cloud computing and an on-premise cluster system for UAS images in agriculture," International Journal of Geo-Information, 10, 677, https://doi.org/10.3390/ijgi10100677, 2021


S. Oh, A. Chang, A. Ashapure, J. Jung, N. Dube, M. Maeda, D. Gonzalez, J. Landivar, "Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework", Remote Sensing, 12(18):2981, DOI: 10.3390/rs12182981, 2020


M. Bhandari, A. Ibrahim, Q. Xue, J. Jung, A. Chang, J. Rudd, M. Maeda, N. Rajan, H. Neely, J. Landivar, "Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)", Computers and Electronics in Agriculture, 176:105665, DOI: 10.1016/j.compag.2020.105665, 2020


Landivar J., J. Jung, A. Ashapure, M. Bhandari, M. Maeda, J. Landivar, A. Chang and D. Gonzalez. 2021. In-Season Management of Cotton Crops Using “Digital Twins” Models. ASA-CSSA-SSSA International Annual Meeting, Salt Lake City, UT, November 9-11, 2021.


Landivar J, M. Maeda, A. Chang, J. Jung, J. McGinty, C. Bednarz, 2021. "Estimating the time and rate of harvest aid chemicals using an Unmanned Aircraft System," 2021 Beltwide Cotton Conferences, Online Conference, January 5 - 7, 2021


Ashapure A., J. Jung, A. Chang, S. Oh, J. Yeom, M. Maeda, A. Maeda, N. Dube, J. Landivar, S. Hague, W. Smith, 2020. "Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 180-194.


J. Jung, M. Maeda, A. Chang, M. Bhandari, A. Ashapure, J. Landivar, "The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems", Current Opinion in Biotechnology, vol. 70, pp. 15-22, 2021


 


 

07/24/2023

09/05/2023

06/11/2024

10/02/2024

Refereed Journals/Book Chapters


Oregon State University



  1. Zhang, Y., Hartemink, A.E., Weerasekara, M., 2023. An automated, web-based soil property and soil health estimation tool using mid-infrared (MIR) spectroscopy and machine learning. National Cooperative Soil Survey Meeting, July 9–13, Bismarck, ND, USA.


 


South Dakota State University, SDSU



  1. Antora, S.S., Chang, Y.K., Nguyen-Quang, T., & Heung, B. (2023). Development and Assessment of a Field-Programmable Gate Array (FPGA)-Based Image Processing (FIP) System for Agricultural Field Monitoring Applications. AgriEngineering, 5(2), 886-904.

  2. Shin, J., Mahmud, M., Rehman, T. U., Ravichandran, P., Heung, B., & Chang, Y.K. † (2023). Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture. AgriEngineering, 5(1), 20-39.

  3. Conference paper

  4. Alahe, M.A., Kemeshi, J., & Chang, Y. (2024) Comparison Between Jetson Nano and Jetson Xavier NX for Ag Data Security. In 2024 ASABE Annual International Meeting. Oral presentation with conference paper (doi: 10.13031/aim.202400811).

  5. Kemeshi, J., Alahe, M.A., Chang, Y., & Yadav, P.K. (2024) Effect of Camera Shutter Mechanism on the Accuracy of a Custom YOLOv8 Model for Pattern Recognition in Motion on a UGV. In 2024 ASABE Annual International Meeting. Oral presentation with conference paper (doi: 10.13031/aim.202400812).

  6. Alahe, M.A., Kemeshi, J., Chang, Y., & Menendez, H. (2024) Sustainable Livestock Management and Pasture Utilization using Automotive Electric Fencing System. In 2024 ASABE Annual International Meeting. Oral presentation with conference paper (doi: 10.13031/aim.202400820).

  7. Kemeshi, J., Gummi, S.R., & Chang, Y. (2024) R2B2 Project: Design and Construction of a Low-cost and Efficient Autonomous UGV For Row Crop Monitoring. 16th ICPA. Oral presentation with conference paper (#10111).

  8. Gummi, S.R., Kemeshi, J., & Chang, Y. (2024) Botanix Explorer (BX1): Precision plant phenotyping robot detecting Stomatal openings for Precision Irrigation and Drought Tolerance experiments. 16th ICPA. Oral presentation with conference paper (#10202).

  9. Kemeshi, J., Chang, Y., Yadav, P.K., & Alahe, M.A. (2024) Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV. 16th ICPA. Oral presentation with conference paper (#10223).

  10. Alahe, M.A., Kemeshi, J., Chang, Y., Won, K., Yang, X., & Sher, M. (2024) Securing Agricultural Data with Encryption Algorithms on Embedded GPU based Edge Computing Devices. 16th ICPA. Oral presentation with conference paper (#10244).

  11. Alahe, M.A., Kemeshi, J., Gummi, S.R., Chang, Y., & Menendez, H. (2024) Design of an Automatic Travelling Electric Fence System for Sustainable Grazing Management. 16th ICPA. Oral presentation with conference paper (#10246).

  12. Gummi, S.R., Alahe, M.A., Kemeshi, J., & Chang, Y. (2024) Securing Agricultural Imaging Data in Smart Agriculture: A Blockchain-Based Approach to Mitigate Cybersecurity Threats and Future Innovations. 16th ICPA. Oral presentation with conference paper (#10247).

  13. Gummi, S.R., Alahe, M.A., Pack, C., & Chang, Y. (2024) A Swarm Robotics Navigation Simulator for Phenotyping Soybean Plants using Voronoi-Ant Colony Optimization. 16th ICPA. Oral presentation with conference paper (#10282).

  14. Brennan, J., Parsons, I., Harrison, M. & Menendez, H. Development of an Application Programming Interface (API) to automate downloading and processing of precision livestock data. (2024).

  15. Brennan, J., Parsons, I., Harrison, M. & Menendez, H. Development of an Application Programming Interface (API) to automate downloading and processing of precision livestock data. ASAS, Calgary Alberta (2024).

  16. Parsons, Ira Lloyd, Brandi B Karisch, Amanda E Stone, Stephen L Webb, Durham A Norman, and Garrett M Street. Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals, 2024.

  17. Wang T., H. Jin, H. Sieverding, S. Kumar, Y. Miao, O. Obembe, X. Rao, A. Nafchi, D. Redfearn, S. Cheye. 2023. “Understanding farmer views of precision agriculture profitability in the US Midwest.” Ecological Economics, 213, 107950.

  18. Wang T., H. Jin, and S. Heidi. 2023. Factors affecting farmer perceived challenges towards precision agriculture. Precision Agriculture. https://doi.org/10.1007/s11119-023-10048-2.

  19. Adereti, D. T., Gardezi, M., Wang, T., McMaine, J. 2023. Understanding farmers’ engagement and barrier to machine learning-based intelligent agricultural decision support systems. Agronomy Journal. https://doi.org/10.1002/agj2.21358.


 LSU



  1. Setiyono, T., Gentimis, T., Rontani, F., Duron, D., Bortolon, G., Adhikari, R., ... & Pitman, W. D. (2024). Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images. Smart Agricultural Technology, 7, 100400.

  2. Santos, L. B., Gentry, D., Tryforos, A., Fultz, L., Beasley, J., & Gentimis, T. (2024). Soybean yield prediction using machine learning algorithms under a cover crop management system. Smart Agricultural Technology, 100442.

  3. Bampasidou, M., Goldgaber, D., Gentimis, T., & Mandalika, A. (2024). Overcoming ‘Digital Divides’: Leveraging higher education to develop next generation digital agriculture professionals. Computers and Electronics in Agriculture, 224, 109181.


Clemson



  1. Koc, A.B., Erwin, C., Aguerre, M., Chastain, J. 2024. Estimating Tall Fescue and Alfalfa Forage Biomass Using an Unmanned Ground Vehicle. 15th International Congress on Agricultural Mechanization and Energy in Agriculture Cham 2024. Lecture Notes in Civil Engineering, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-51579-8_32. Publisher: Springer Nature Switzerland Pages: 357-372.

  2. Singh, J., Koc, A.B., Aguerre, M.J., Chastain, J.P., and Shaik, S. 2024. Estimating Bermudagrass Aboveground Biomass Using Stereovision and Vegetation Coverage. Remote Sensing, 16, 2646. https://doi.org/10.3390/rs16142646 .

  3. Koc, A.B., Erwin, C., Aguerre, M., Chastain, J. 2023. Estimating Tall Fescue and Alfalfa Forage Biomass Using an Unmanned Ground Vehicle. 15ᵗʰ International Congress of Agricultural Mechanization and Energy in Agriculture (AnkAgEng'23 - Antalya-Turkiye, Oct. 29 - Nov. 2,2023).

  4. Koc, A. B., Singh, J., Aguerre, M. J. (2023). Estimating forage biomass using unmanned ground and aerial vehicles. In Proceedings of International Grassland Congress 2023. Pp. 1449-1452. https://doi.org/10.52202/071171-0352 .


 


MSU



  1. Ahmed, T., Wijewardane, N., Lu, Y., Jones, D., Kudenov, M., Williams, C., Villordon, A., Kamruzzaman, M., 2024. Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence. Computers and Electronics in Agriculture 220, 108855.

  2. Xu, J., Lu, Y., 2024. Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes. Computers and Electronics in Agriculture 219, 108826.

  3. Xu. J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a machine vision-based automated sweetpotato grading and sorting system. Journal of the ASABE (under review).

  4. Xu, J., Lu, Y., Deng, B., 2024. OpenWeedGUI: an open-source graphical tool for weed Imaging and YOLO-based weed detection. Electronics 13 (9), 1699. (Project#2, Lu)

  5. Deng, B., Lu, Y., 2024. Canopy Image-based Blueberry Detection by YOLOv8 and YOLOv9. Artificial Intelligence in Agriculture (under review).

  6. Wang, Y., Lu, Y., Morris, D., Benjamin, M., Lavagnino, M., McIntyre, J., 2024. Automated sow body condition estimation by 3D computer vision towards precision livestock farming. Artificial Intelligence in Agriculture (submitted to journal).

  7. Olaniyi, E., Lu, Y., Sukumaran, A., Jarvis, T., Clinton, R., 2023. Non-destructive Assessment of White Striping in Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Deep Learning. Journal of the ASABE 66(6), 1437-1447.

  8. Dang, F., Chen, D., Lu, Y., Li, Z., 2023. YOLOWeeds: a novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture 205, 107655.

  9. Chen, D., Qi, X., Zheng, Y., Lu, Y., Huang, Y., Li, Z., 2024. Synthetic data augmentation by diffusion probabilistic models to enhance weed recognition. Computers and Electronics in Agriculture 216, 108517.

  10. Deng, B., Lu, Y., Xu, J., 2024. Weed database development: An updated survey of public weed datasets and cross-season weed detection adaptation. Ecological Informatics, 102546.


 


UArk



  1. Li, Z., Wang, D., Zhu, T., Tao, Y., & Ni, C. (2024). Review of deep learning-based methods for non-destructive evaluation of agricultural products. Biosystems Engineering, 245, 56-83.

  2. Wang, D., Sethu, S., Nathan, S., Li, Z., Hogan, V. J., Ni, C., ... & Seo, H. S. (2024). Is human perception reliable? Toward illumination robust food freshness prediction from food appearance—Taking lettuce freshness evaluation as an example. Journal of Food Engineering, 112179.

  3. Zhou, C., Li, Z., Wang, D., Xue, S., Zhu, T., & Ni, C. (2024). SSNet: Exploiting Spatial Information for Tobacco Stem Impurity Detection with Hyperspectral Imaging. IEEE Access.

  4. Ali, M. A., Wang, D., & Tao, Y. (2024). Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines. Sensors, 24(8), 2385.

  5. Xu, Z., Uppuluri, R., Shou, W., Wang, D., & She, Y. (2024). Whole Chicken Pushing Manipulation via Imitation Learning. In 2024 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers.

  6. Li, Z., Wang, D., Zhu, T., Ni, C., & Zhou, C. (2023). SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut. Infrared Physics & Technology, 132, 104731.


 


UF



  1. da Cunha V.G., A. Hariharan J., Ampatzidis Y., Roberts P., 2023. Early detection of tomato bacterial spot disease in transplant tomato seedlings utilizing remote sensing and artificial intelligence. Biosystems Engineering, 234, 172-186, https://doi.org/10.1016/j.biosystemseng.2023.09.002.

  2. da Cunha V.A.G., Pullock D., Ali M., Neto A.D.C., Ampatzidis Y., Weldon C., Kruger K., Manrakhan A., Qureshi J., 2024. Psyllid Detector: a web-based application to automate insect detection utilizing image processing and artificial intelligence. Applied Engineering in Agriculture, 40(4), 427-439. https://doi.org/10.13031/aea.15826. 

  3. Javidan S.M., Banakar A., Rahnama K., Vakilian K.A., Ampatzidis Y., Feature engineering to identify plant diseases using image processing and artificial intelligence: a comprehensive review. Smart Agricultural Technology, 8, 100480, https://doi.org/10.1016/j.atech.2024.100480.

  4. Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., Rahnama K., 2024. Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning. Multimedia Tools and Applications, 1-19, https://doi.org/10.1007/s11042-024-18214-y. 

  5. Kim, D.W., S.J. Jeong, S. Lee, H. Yun, Y.S., Chung, Y.-S. Kwon, and H.-J. Kim. 2023. Growth monitoring of field-grown onion and garlic by CIE L*a*b* color space and region-based crop segmentation of UAV RGB images. Precision Agric 24, 1982–2001. https://doi.org/10.1007/s11119-023-10026-8.

  6. Kondaparthi AK, Lee WS, Peres NA. Utilizing High-Resolution Imaging and Artificial Intelligence for Accurate Leaf Wetness Detection for the Strawberry Advisory System (SAS). Sensors. 2024; 24(15):4836. https://doi.org/10.3390/s24154836.

  7. Liu X., Zhang Z., Igathinathane C., Flores P., Zhang M., Li H., Han X., Ha T., Ampatzidis Y., Kim H-J., 2024. Infield corn kernel detection using image processing, machine learning, and deep learning methodologies. Expert Systems with Applications, 238 (part E), 122278, https://doi.org/10.1016/j.eswa.2023.122278.

  8. Mehdizadeh S.A., Noshad M., Chaharlangi M., Ampatzidis Y., Development of an innovative optoelectronic nose for detecting adulteration in quince seed oil. Foods, 12(23), 4350, https://doi.org/10.3390/foods12234350.

  9. Mirbod, O., Choi, D., Heinemann, P. H., Marini, R. P., & He, L. (2023). On-tree apple fruit size estimation using stereo vision with deep learning-based occlusion handling. Biosystems Engineering, 226, 27-42.

  10. Ojo I., Ampatzidis Y., Neto A.D.C., Batuman O., 2024. Development of an automated needle-based trunk injection system for HLB-affected citrus trees. Biosystems Engineering, 240, 90-99, https://doi.org/10.1016/j.biosystemseng.2024.03.003.

  11. Ojo I., Ampatzidis Y., Neto A.D.C., Bayabil K.H., Schueller K.J., Batuman O., 2024. Determination of needle penetration force and pump pressure for the development of an automated trunk injection system for HLB-affected citrus trees. Journal of ASABE, 67, 4, https://doi.org/10.13031/ja.15975.

  12. Teshome F.T., Bayabil H.K., Hoogenboom G., Schaffer B., Singh A., Ampatzidis Y., Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping. Computers and Electronics in Agriculture, 212, 108064, https://doi.org/10.1016/j.compag.2023.108064.

  13. Teshome F.T., Bayabil H.K., Schaffer B., Ampatzidis Y., Hoogenboom G., Singh A., 2024. Simulating soil hydrologic dynamics using crop growth and machine learning models. Computers and Electronics in Agriculture, 224, 109186, https://doi.org/10.1016/j.compag.2024.109186.

  14. Zhang L., Ferguson L., Ying L., Lyons A., Laca E., and Ampatzidis Y., Developing a web-based pistachio nut growth prediction system for orchard management. HortTechnology, 34,1, 1-7, https://doi.org/10.21273/HORTTECH05270-23.

  15. Zhou, C., S. Lee, O. E. Liburd, I. Aygun, X. Zhou, A. Pourreza, J. K. Schueller, Y. Ampatzidis. 2023. Detecting two-spotted spider mites and predatory mites in strawberry using deep learning. Smart Agricultural Technology, 4, 100229. https://doi.org/10.1016/j.atech.2023.100229.

  16. Zhou C., S. Lee, S. Zhang, O. E. Liburd, A. Pourreza, J. K. Schueller, Y. Ampatzidis. 2024. A smartphone application for site-specific pest management based on deep learning and spatial interpolation. Computers and Electronics in Agriculture, 218, 2024, 108726, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2024.108726.

  17. De Vries, A., Bliznyuk, N., & Pinedo, P. (2023). Invited Review: Examples and opportunities for artificial intelligence (AI) in dairy farms. Applied Animal Science, 39(1), 14-22.

  18. Kalopesa, E., Tziolas, N., Tsakiridis, N., Multimodal Fusion for soil organic carbon estimation at continental scale. Remote Sensing. (submitted)

  19. Rosin, N. A., Demattê, J. A. M., Carvalho, H. W. P., Rodriguez-Albarracín, H. S., Rosas, J. T. F., Novais, J. J., Dalmolin, R. S. D., Alves, M. R., Falcioni, R., Tziolas, N., Mallah, S., de Mello, D. C., & Francelino, M. R. (2024). Spatializing soil elemental concentration as measured by X-ray fluorescence analysis using remote sensing data. Catena, 240, 107988. https://doi.org/10.1016/j.catena.2024.107988  

  20. Tziolas, N., Tsakiridis, N., Heiden, U., & van Wesemael, B. (2024). Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data: A case study in Bavaria state, Germany. Geoderma, 444, 116867. https://doi.org/10.1016/j.geoderma.2024.116867

  21. Patnam Reddy, K., Tziolas, N., Dematte, J., AI-driven online spectral analysis tool for global use. Geoderma. (being prepared).

  22. Qian, H., McLamore, E., & Bliznyuk, N. (2023). Machine learning for improved detection of pathogenic E. coli in hydroponic irrigation water using impedimetric aptasensors: A comparative study. ACS omega, 8(37), 34171-34179.


 


Mississippi State University



  1. Gharakhani, H., Thomasson, J. A., Lu, Y., & Reddy, K. R. (2023). Field Test and Evaluation of an Innovative Vision-Guided Robotic Cotton Harvester. Computers and Electronics in Agriculture. 225: 109314.


 


UTK



  1. Amirivojdan, A., Nasiri, A., Zhou, S., Zhao, Y., & Gan, H. (2024). ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology. AgriEngineering, 6(3), 2115-2129.

  2. Nasiri, A., Zhao, Y., & Gan, H. (2024). Automated detection and counting of broiler behaviors using a video recognition system. Computers and Electronics in Agriculture, 221, 108930. DOI: 10.1016/j.compag.2024.108930

  3. Nasiri, A., Amirivojdan, A., Zhao, Y., & Gan, H. (2024). An automated video action recognition-based system for drinking time estimation of individual broilers. Smart Agricultural Technology, 100409. https://DOI:10.1016/j.atech.2024.100409


UK



  1. Ekramirad, N., Doyle, L.E., Loeb, J.R., Santra, D., Adedeji, A.A. (2024). Hyperspectral imaging and machine learning as a nondestructive method for proso millet seed detection and classification. Foods 13(9), 1330.

  2. Adedeji, A.A, Ekramirad, N., Khaled, Y.A., and Villanueva, R. (2024). Impact of storage on nondestructive detectability of codling moth infestation in apples. Journal of ASABE 67(2):401-408. https://doi.org/10.13031/ja.15583. JIF

  3. Tizhe Liberty, J., Sun, S., Kucha, C., Adedeji, A. A., Agidi, G., & Ngadi, M. O. (2024). Augmented reality for food quality assessment: Bridging the physical and digital worlds. Journal of Food Engineering 367, 111893. https://doi.org/10.1016/j.jfoodeng.2023.111893

  4. Adedeji, A.A., Okeke, A., and Rady, A. (2023). Utilization of FTIR and machine learning for evaluating gluten-free bread contaminated with wheat flour. Sustainability – Food Processing Safety and Public Health 15(11), 8742.

  5. Khaled, Y.A., Ekramirad, N., Donohue, K., Villanueva, R., and Adedeji, A.A. (2023). Non-destructive hyperspectral imaging and machine learning-based predictive models for physicochemical quality attributes of apples during storage as affected by codling moth infestation. Agriculture – Digital Agriculture 13(5),1086. https://doi.org/10.3390/agriculture13051086.

  6. Ekramirad, N., Khaled, Y.A., Donohue, K., Villanueva, R., and Adedeji, A.A. (2023). Classification of codling moth infested apples using sensor data fusion of acoustic and hyperspectral features coupled with machine learning. Agriculture - Agricultural Technology 13(4), 839. https://doi.org/10.3390/agriculture13040839.


 


TAMU



  1. Fernandes, M.M., Fernandes Junior, J.d., Adams, J.M., Tedeschi, L.O. et al.(2024). Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content. Scientific Report. 14, 8704. https://doi.org/10.1038/s41598-024-59160-x

  2. Kaniyamattam, Bhandari, M., Hardin, R., Tao, J., Landivar, J., and Tedeschi, L. (2023). Scalable Data-driven Intelligent Agri-Systems: Opportunities, Challenges, and Research Investment Analysis for the State of Texas. A white paper submitted to Texas A&M AgriLife Research.

  3. Risal, A., Niu, H., Landivar-Scott, J. L., Maeda, M. M., Bednarz, C. W., Landivar-Bowles, J., ... & Bhandari, M. (2024). Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains. Water, 16(9), 1300.

  4. Niu, H., Peddagudreddygari, J. R., Bhandari, M., Landivar, J. A., Bednarz, C. W., & Duffield, N. (2024). In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery. Sensors, 24(8), 2432.

  5. Khuimphukhieo, I., Bhandari, M., Enciso, J., & da Silva, J. A. (2024). Assessing Drought Stress of Sugarcane Cultivars Using Unmanned Vehicle System (UAS)-Based Vegetation Indices and Physiological Parameters. Remote Sensing, 16(8), 1433.

  6. Zhao, L., Bhandari, M., Um, D., Nowka, K., Landivar, J., & Landivar, J. Cotton Yield Prediction Utilizing Unmanned Aerial Vehicles (Uav) and Bayesian Neural Networks. Available at SSRN 4693599.

  7. Dhal, S. B., Kalafatis, S., Braga-Neto, U., Gadepally, K. C., Landivar-Scott, J. L., Zhao, L., ... & Bhandari, M. (2024). Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops. Remote Sensing, 16(11), 1906.

  8. Happs, R. M., Hanes, R. J., Bartling, A. W., Field, J. L., Harman-Ware, A. E., Clark, R. J., Yaping, X., ... & Davison, B. H. (2024). Economic and Sustainability Impacts of Yield and Composition Variation in Bioenergy Crops: Switchgrass (Panicum virgatum L.). ACS Sustainable Chemistry & Engineering, 12(5), 1897-1910.

  9. Bhandari, M., Chang, A., Jung, J., Ibrahim, A. M., Rudd, J. C., Baker, S., ... & Landivar, J. (2023). Unmanned aerial system‐based high‐throughput phenotyping for plant breeding. The Plant Phenome Journal, 6(1), e20058.


K-State



  1. McGinty H, Shimizu C, Hitzler P, & Sharda A. (2024). Towards a Global Food Systems Datahub. Semantic Web -1 (2024) 1–4. https://DOI.org/10.3233/SW-243688

  2. Badua S, Sharda A, Aryal B. 2024. Quantifying real-time opening disk load during planting operations to assess compaction and potential for planter control. Precision Agriculture25(4):1-13. https://DOI.org/1007/s11119-024-10151-y   

  3. Das S, Flippo D, Welch S. 2024. Autonomous robot system for steep terrain farming operations. U.S. Patent and Trademark Office. 

  4. Grijalva I, Kang Q, Flippo D, Sharda A, McCornack B. 2024. Unconventional strategies for aphid management in sorghum. Insects, 15(475).

  5. Rahman R, Indris C, Bramesfeld G, Zhang T, Li K, Chen X, Grijalva I, McCornack B, Flippo D, Sharda A, Wang G. A new dataset and comparative study for aphid cluster detection and segmentation in sorghum fields. Journal of Imaging, 10(5), 2024-5-08.

  6. Pokharel P, Sharda A, Flippo D, Ladino K. Design and systematic evaluation of an under-canopy robotic spray system for row crops.  Smart Agricultural Technology, 8:100510.


ALL STATION CONFERENCE PRESENTATIONS: PODIUM/POSTER


SDSU



  1. Wang, T. and H. Jin. Factors Affecting Farmer Adoption of Unmanned Aerial Vehicles: Current and Future. 2024 AI in Agriculture and Natural Resources Conference. April 15-17, 2024, College Station, Texas.

  2. Wang, T. and H. Jin. Factors Affecting Farmer Adoption of Unmanned Aerial Vehicles: Current and Future. Southern Agricultural Economics Association (SAEA) 56th Annual Meeting. February 3-6, 2024, Atlanta, Georgia.

  3. Adereti, D. T., Gardezi, M., Wang, T., McMaine, J. 2023. Understanding farmers’ engagement and barrier to machine learning-based intelligent agricultural decision support systems. 85th Annual Meeting of the Rural Sociological Society. August 2-6, Burlington, VT.


 


LSU



  1. Adhikari, R., Setiyono, T., Dodla, S. K., Pabuayon, I. L., Duron, D., Acharya, B., ... & Shiratsuchi, L. S. (2023, October). Evaluation of Varying Canopy Distance on Crop Circle Phenom Sensor Measurements: Implications for Remote Sensing of Crop Parameters. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA

  2. Setiyono, T., Dodla, S. K., Rontani, F. A., Acharya, B., Duron, D., Adhikari, R., ... & Gentimis, T. (2023, October). Precision Positioning in UAV Remote Sensing: Case Study in Corn N Rates and Soybean Seeding Rates Experiments. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.

  3. Acharya, B., Setiyono, T., Rontani, F. A., Dodla, S. K., Adhikari, R., Duron, D., ... & Parvej, R. (2023, October). Application of UAV Remote Sensing for Monitoring Nitrogen Status in Corn Under Excessive Rainfall Conditions. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.

  4. Duron, D., Rontani, F. A., Acharya, B., Adhikari, R., Taylor, Z., Blanchard, B., ... & Setiyono, T. (2023, October). Integrating Crop Modeling and Remote Sensing Data for Prediction of Sugarcane Growth, Yield, and Sugar Content and Their Field Spatial Variability. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.

  5. Adhikari, R., Setiyono, T., Dodla, S. K., Pabuayon, I. L., Duron, D., Acharya, B., ... & Shiratsuchi, L. S. (2023, October). Multi-Sensor Crop Sensing Platforms for Monitoring Agronomic Practices Under Different Tillage and Fertilization Systems. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.

  6. Lanza, P., Santos, L., Gentimis, A., Yang, Y., Conger, S., & Beasley, J. (2023). Parameters to increase LiDAR mounted UAV efficiency on agricultural field elevation measurements. In Precision agriculture'23(pp. 715-721). Wageningen Academic.

  7. Júnior, M. R. B., de Almeida Moreira, B. R., Duron, D., Setiyono, T., Shiratsuchi, L. S., & da Silva, R. P. (2024). Integrated sensing and machine learning: Predicting saccharine and bioenergy feedstocks in sugarcane. Industrial Crops and Products, 215, 118627.


Clemson



  1. Singh, J., Koc, A.B., Aguerre, M.J., Chastain, J.P., and Shaik, S. 2024. Stereoscopic Morphometry in Forages: Predicting Pasture Quantity with Field Robotics. Presented at the 2024 ASABE Annual International Meeting, July 28-31, 2024. Anaheim CA.

  2. Lisa Umutoni, Vidya Samadi, George Vellidis, Jose Payero, Bulent Koc, Charles Privette III. 2024. Application of Deep Neural Networks for Seasonal Cotton Yield Estimation. Presented at the 2024 ASABE Annual International Meeting, July 28-31, 2024. Anaheim CA.

  3. Shaik, S., B. Koc, J. Singh, M. Aguerre, J. P. Chastain. 2024. Aboveground Biomass Prediction of Bermudagrass: A Comparative Analysis of Machine Learning Models. 2024 AI in Agriculture and Natural Resources Conference. April 15, 2024 - April 17, 2024.


 


MSU



  1. Xu, J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a machine vision-based automated sweet potato grading and sorting System. ASABE Annual International Meeting 2400102.

  2. Xu, J., Lu, Y, 2024. Design and preliminary evaluation of a machine vision-based automated sweet potato sorting system. Sensing for Agriculture and Food Quality and Safety XVI Proceedings Volume PC13060.

  3. Xu., J., Lu, Y., 2024. Prototyping and preliminary evaluation of a real-time multispectral vision system for automated sweet potato quality grading. Presented at the 2024 International Conference on Precision Agriculture. (Project #1, Lu)

  4. Xu, J., Lu, Y., 2023. OpenWeedGUI: an open-source graphical user interface for weed imaging and detection. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 12539, 97-106.

  5. Deng, B., Lu, Y., Vander Weide, J., 2024. Development and Preliminary Evaluation of a Deep Learning-based Fruit Counting Mobile APP for High-bush Blueberries. ASABE Annual International Meeting 2401022

  6. Wang, Y., Lu, Y., Morris, D., Benjamin, M., Lavagnino, M., McIntyre, J., 2024. 3D Computer Vision-Based Sow Body Condition Estimation Towards Precision Livestock Farming. Presented at the 2024 AI Conference in Agriculture.

  7. Wang, Y., Lu, Y., Morris, D., Benjamin, M., Lavagnino, M., McIntyre, J., 2024. 3D Computer Vision with A Spatial-Temporal Neural Network for Lameness Detection of Sows. Presented at the 2024 International Conference on Precision Agriculture.

  8. Deng, B., Lu, Y., 2023. Factors influencing the detection of Lambsquarters by YOLOv8 towards precision weed control. Poster presented at the Great Lakes EXPO (Grand Rapids, Michigan).

  9. Deng, B., Lu, Y., 2024. Weed Image Augmentation by ControlNet-Added Stable Diffusion. Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II 130350M. https://doi.org/10.1117/12.3014145


 


UArk



  1. Pallerla C., Owens, C., Wang D., (2024) Hyperspectral imaging and Machine learning algorithms for foreign material detection on the chicken surface. In 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting.Anaheim, CA [Poster presentation]

  2. Pallerla C., Owens, C., Wang D., (2024) Hyperspectral imaging and Machine learning algorithms for foreign material detection on the chicken surface. In 2024 Poultry Science Asscoiation Annual International Meeting. Louisville, KY [Poster presentation]

  3. Feng Y., Wang D., (2024) Synthetic Data Augmentation for Chicken Carcass Instance Segmentation with Mask Transformer. In 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Anaheim, CA [Poster presentation]

  4. Mahmoudi S., Wang D., (2024) Automated Solutions for Poultry Processing: Integrating Robotic Swab Sampling and Pathogen Detection Technologies. In 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Anaheim, CA [Poster presentation]

  5. Sohrabipour P., Wan S., Yu S., Wang D., (2024) Depth image guided Mask-RCNN model for chicken detection in poultry processing line. In 2024 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting.Anaheim, CA [Oral presentation]

  6. Mahmoudi S., Sohrabipour P., Obe T., Gibson K., Crandall P., Jeyam S., Wang D. (2024), Automated Environmental Swabbing: A Robotic Solution for Enhancing Food Safety in Poultry Processing. In 2024 the Third Annual Artificial Intelligence in Agriculture Conference. College Station, TX [Poster presentation]

  7. Sohrabipour P., Mahmoudi S., She Y., Shou W., Pallerla C., Schrader L., Wang D. (2024), Advanced Poultry Automation: Integrating 3D Vision Reconstruction and Mask R-CNN for Efficient Chicken Handling. In 2024 the Third Annual Artificial Intelligence in Agriculture Conference. College Station, TX [Poster presentation, First place winner]


 


UF



  1. Ampatzidis Y., 2024. Can AI and automation transform specialty crop production? 16th International Conference on Precision Agriculture (ICPA), International Symposium on robotics and Automation, Manhattan, Kansas, USA, July 21-24.

  2. Ampatzidis Y., 2024. Agroview and Agrosense for AI-enhanced precision orchard management. SE Regional Fruit and Vegetable Conference, Savannah, GA, January 11-14, 2024

  3. Ampatzidis Y., 2023. Emerging and advanced technologies in agriculture. Link (Linking Industry Networks through Certifications; High School Teachers Training) Conference, Daytona Beach, FL, October 10-12, 2023.

  4. Ampatzidis Y., 2023. AI and Extension. Possibilities and Challenges. 2023 SR-PLN Middle Managers Conference, Next Generation: Evolving the Extension Enterprise, Orlando, FL, August 22-24.

  5. Ampatzidis Y., 2023. AI-Enhanced Technologies for Precision Management of Specialty Crops. Sustainable Precision Agriculture in the Era of IoT and Artificial Intelligence, Bard Ag-AI Workshop, Be’er Sheva, Israel, July 18-20, 2023.

  6. Ampatzidis Y., Ojo I., Neto A.D.C., Batuman O., 2024. Automated needle-based trunk injection system for HLB-affected citrus trees. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.

  7. Ampatzidis Y., Vijayakumar V., Pardalos P., 2024. AI-enabled robotic spraying technology for precision weed management in specialty crops. Optimization, Analytics, and Decision in Big Data Era Conference (in honor of the 70th birthday of Dr. Panos Pardalos), Halkidiki, Greece, June 16-21.

  8. Banakar A., Javidan S.M., Vakilian K.A., Ampatzidis Y., 2024. Detection of spectral signature and classification of Alternaria alternata and Alternaria solani diseases in tomato plant by analysis of hyperspectral images and support vector machine. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.

  9. Cho Y., Yu, Z., Ampatzidis Y., Nam J., 2024. Blockchain-enhanced security and data management in smart agriculture. 6th CIGR International Conference, Jeju, Korea, May 19–23, 2024.

  10. Dutt, N., & Choi, D. (2024). A Computer Vision System for Mushroom Detection and Maturity Estimation using Depth Images. 2024 ASABE Annual International Meeting.

  11. Etefaghi, A., Medeiros, H. “ViLAD: Video-based Lettuce Association and Detection ,” American Society of Agricultural and Biological Engineers Annual International Meeting, Anaheim, CA, July 2024.

  12. Gallios, I., & Tziolas, N. (2024). Synergistic use of low-cost NIR scanner and geospatial covariates to enhance soil organic carbon predictions using dual input deep learning techniques. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 8-12 July, Athens, Greece.

  13. Hernandez, B., Medeiros, H. “Multiple Plant Tracking for Robotized Spraying of Ground Plants,” 2023 IROS Workshop on Agricultural Robotics for a Sustainable Future Innovation in Precision Agriculture (3rd paper prize), Detroit, MI, Oct 2023.

  14. Huang, Z., W. S. Lee, N.C. Takkellapati. 2024. Strawberry canopy size estimation with SAM guided by YOLOv8 detection. ASABE Paper No. 2400181. St. Joseph, MI.: ASABE.  

  15. Huang, Z., W. S. Lee, N.C. Takkellapati. 2024. HOPSY: Harvesting Optimization for Production of StrawberrY using real-time detection with modified YOLOv8-nano. In Proceedings of the 16th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL: International Society of Precision Agriculture.

  16. Ilodibe, U., & Choi, D. (2024). Evaluating The Performance of a Mite Dispensing System for Biological Control of Chilli Thrips in Strawberry Production in Florida. 2024 ASABE Annual International Meeting.

  17. Lacerda C., and Neto A.D.C., Ampatzidis Y., 2024. Agroview: enhance satellite imagery using super-resolution and generative AI for precision management in specialty crops. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.

  18. Lee, W. S. 2023. Strawberry plant wetness detection using color imaging and artificial intelligence for the Strawberry Advisory System (SAS). 2023 Annual Strawberry AgriTech Conference, Plant City, FL, May 17, 2023.

  19. Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Chungnam National University, Daejeon-si, Korea. May 24, 2023.

  20. Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Department of Agricultural Engineering, Division of Smart Farm Development, National Institute of Agricultural Sciences, Jeonju-si, Korea. May 25, 2023.

  21. Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Seoul National University, Seoul, Korea. May 31, 2023.

  22. Lee, W. S., Y. Ampatzidis, and D. Choi. 2023. University of Florida 2023 W-3009 Report (presented via Zoom). Cornell AgriTech, Cornell

  23. Mirbod, O., & Choi, D. (2023). Synthetic Data-Driven AI Using Mixture of Rendered and Real Imaging Data foUniversity, Geneva, NY. June 20-21, 2023.

  24. Medeiros, H. “Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Detroit, MI, Oct 2023.r Strawberry Yield Estimation. 2023 ASABE Annual International Meeting.

  25. Ojo I., Neto A.D.C., Ampatzidis Y., Batuman O., Albrecht U., 2024. Needle-based, automated trunk injection system for HLB-affected citrus trees. International Research Conference on Huanglongbing VII, Riverside, CA, March 26-29, 2024.

  26. Ottoy, S., Karyotis, K., Kalopesa, E., Van Meerbeek, K., Nedelkou, J., Gkrimpizis, T., De Vocht, A., Zalidis, G., & Tziolas, N. (2024). Digital mapping of soil organic carbon using drone remote sensing. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 8-12 July, Athens, Greece.

  27. Vijayakumar V., Ampatzidis Y., 2024. Development of a machine vision and spraying system of a robotic precision smart sprayer for specialty crops. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.

  28. Wang, R., Hofstetter, D. Medeiros, H. Boney, J. Kassub, H. “Evaluation of turkey behavior under different night lighting treatments using machine learning.” American Society of Agricultural and Biological Engineers Annual International Meeting, Anaheim, CA, July 2024.

  29. Zhou C., Ampatzidis Y., Pullock D., 2024. Detecting citrus pests from sticky traps using deep learning. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.

  30. Zhou, X., Y. Ampatzidis, W. S. Lee, S. Agehara, and J. K. Schueller. 2023. AI-based inspection system for mechanical strawberry harvesters. AI in Agriculture: Innovation and discovery to equitably meet producer needs and perceptions Conference, Orlando, FL, April 17-19, 2023.

  31. Zhou, C., W. S. Lee, W. Kratochvil, J. K. Schueller, and A. Pourreza. 2023. A portable imaging device for twospotted spider mite detection in strawberry. ASABE Annual Meeting, Omaha, NE, July 9-12, 2023.

  32. Zhou, C., W. S. Lee, N. Peres, B. S. Kim, J. H. Kim, and H. C. Moon. 2023. Strawberry flower and fruit detection based on an autonomous imaging robot and deep learning. 14th European Conference on Precision Agriculture, Bologna, Italy, July 2-6, 2023.


UTK



  1. Nasiri, A., Zhao, Y., Gan, H. (2024). Automated broiler behaviors measurement through deep learning models. ASABE Annual International Meeting, Anaheim, CA.

  2. Amirivojdan, A., Nasiri, A., Zhao, Y., Gan, H. (2024). A machine vision system for broiler body weight estimation. ASABE Annual International Meeting, Anaheim, CA.


UCDavis



  1. Li, Z; Karimzadeh, S.; Ahamed, M. S. (2024). Detection of Calcium Deficiency in the Growing Stage of Lettuce Using Computer Vision. ASABE Annual Meeting 2024, July 28-31, Anaheim, California.

  2. Karimzdeh, S.; Chowdhury, M.; Ahamed, M. S. (2023). Fault Detection and Diagnosis of Hydroponic System using Intelligent Computational Model. ASABE Annual Meeting, July 9-12, Omaha, Nebraska.

  3. Li, Z; Karimzadeh, S.; Ahamed, M. S. (2024). Nutrient Dosing Algorithms to Mitigate Ion Imbalance in Closed-Loop Hydroponic Systems. ASABE Annual Meeting 2024, July 28-31, Anaheim, California.


UK



  1. Mizuta K., Miao Y, Lu J, and Negrini R. (2024) Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data. 16th International Conference on Precision Agriculture, Manhattan, KS.

  2. Miao Y, Kechchour A, Sharma V, Flores A, Lacerda L, Mizuta K, Lu J, and Huang Y. (2024) In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote Sensing. 16th International Conference on Precision Agriculture, Manhattan, KS.

  3. Oloyede, A. and Adedeji, A.A. (2024). Near-infrared hyperspectral imaging sensing for gluten detection and quantification. Accepted for presentation at 2024 ASABE Annual International Meeting, Anaheim, CA. July 28 – 31, 2024. Paper #: 2400053.

  4. Adedeji, A.A, Loeb, J.R., Doyle, L.E., Ekramirad, N., and Khaled, Y. Al Fadhl. (2023). Photon-induced reduction in barley malt processing time and quality improvement. A paper presented (oral) at the 14th International Congress on Engineering and Food (ICEF14) held in the city of Nantes France from June 20 – 23, 2023.

  5. Adedeji, A.A., Ekramirad, N., Al Khaled, Y.A., Donohue, K., and Villanueva, R. (2023). Sensor data fusion and machine learning approach for pest infestation detection in apples. A poster presented at the SEC Conference with the theme: “USDA-NIFA AI in Agriculture: Innovation and Discovery to Equitably Meet Producers’ Needs and Perceptions” held in Orlando Florida on April 17 – 19, 2023.


K-State



  1. Alamdari S, Brokesh 2024. “Enhancing Soil Health Monitoring in Precision Agriculture: A Comparative Analysis of avDAQ Vibration Data Collection System and Traditional Soil Sensors” ASABE-AIM, Presentation # 2400896

  2. Peiretti J, Sharda A. “Experimental study on the impact of planter tool bar position on row unit behavior” ASABE-AIM, Presentation # 2400215

  3. Vail B, Brokesh E. “Design and field-testing of a pull-force measuring frame for the testing of agricultural tire rolling resistance” ASABE-AIM, Presentation # 2401007

  4. Shende K, Sharda A. “Integration & testing of wireless data communication system for autonomous liquid application platform” ASABE-AIM, Presentation # 2400833

  5. Kaushal S, Sharda A. “Enhancing Agricultural Feedback Analysis through VUI and Deep Learning Integration” ASABE-AIM, Presentation # 2400287

  6. Abon J, Sharda A. “Optimizing Corn Irrigation Strategies: Insights from ND VI Trends, Soil Moisture Dynamics, and Remote Sensing” ASABE-AIM, Presentation # 2400814

  7. Peiretti J, Sharda A. “Effective Strategies for Closing Furrows Based on Corn Planter Settings” ASABE-AIM, Presentation # 2400215


 


Extension Articles


UF



  1. Choi, D., Mirbod, O., Ilodibe, U., & Kinsey, S. (2023). Understanding Artificial Intelligence: What It Is and How It Is Used in Agriculture: AE589, 10/2023. EDIS,2023(6).

  2. Her Y.G., Bliznyuk N., Ampatzidis, Yu Z., and Bayabil H., 2024. Introduction to Artificial Intelligence in Agriculture. EDIS, University of Florida, IFAS Extension (accepted).

  3. Sharma L., and Ampatzidis Y., Approaches to consider for site-specific field mapping. SS713, EDIS, University of Florida, IFAS Extension, doi.org/10.32473/edis-SS713-2023.


 


 

05/25/2025

N/A

05/25/2025

N/A

09/22/2025

Refereed Journals/Book Chapters


KSU:



  1. Ravinder Singha*, Sehijpreet Kaurb, Deepak R. Joshi, et al. (2025). Estimating Cotton Biomass and Nitrogen Content using Satellite and UAV Data Fusion with Machine Learning. Smart Agriculture Technology.

  2. Tulsi P. Kharel*, Heather L. Tyler, Partson Mubvumba, et al. (2025). Machine learning on multi-spectral imagery to estimate nutrient yield of mixed species cover crops. Agricultural & Environmental Letters.

  3. Deepak R. Joshi, David E. Clay*, Ron Alverson, et al. (2025). Tillage intensity reductions when combined with yield increases may slow soil carbon saturation in the central United States. Scientific Reports.

  4. Dipankar Mandal, Raj Khosla*, Louis Longchanps and Deepak R. Joshi. (2025).Soil Moisture Sensor Location-allocation using Spatial Association of Surface Moisture Data. Smart Agricultural Technology.

  5. Janet Moriles-Miller, Deepak R. Joshi, Graig Reicks, et al. (2025). Delaying Cover Crop Termination Reduced Corn Yields in a Dry Spring. Agrosystems, Geosciences & Environment.

  6. Rai, S., R. Slichter, A. Dalal, A. Sharda. (2025) Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time. Precision agriculture ‘25.  Pages 1038-1044.

  7. Skye Brugler, David E. Clay, Deepak R. Joshi, Thandi Nleya, Garry Hatfield and Sharon A. Clay. (2025). Does splitting the nitrogen rate reduce carbon equivalents? Agronomy Journal.

  8. Shailesh Pandit, Graig Reicks, Janet Moriles-Miller, et al. (2025). Rye as a Cover Crop Reduces Methane Emission Until its Termination. Agronomy Journal.

  9. Mansur, H., Gadhwal, M., Abon, J. E., & Flippo, D. (2025). Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV. Agriculture, 15(8), 882.

  10. Rahman, R., Indris, C., Bramesfeld, G., et al. (2024). A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields. Journal of Imaging, 10(5), 114.

  11. Pokaharel, P., A. Sharda, D. Flippo, K. Ladino (2024). Design and systematic evaluation of an under-canopy robotic spray system for row crops. Smart Agriculture Technology, Vol. 8 100510 ISSN 2772-3755.

  12. Hasib Mansur, Stephen Welch, Daniel Flippo. (2025). A novel way of using flex sensors as tactile sensors in agricultural robotics: A proof-of-concept study. TechRxiv. May 01, 2025.

  13. Singh, R., J. Fabula, K. Shende, A. Sharda. (2025). Spray Coverage and Droplet Size Uniformity of Pulse Width Modulation (PWM) Systems at Different Duty Cycles and Frequencies. Applied Engineering in Agriculture. 41(2): 119-124. 


 


LSU:



  1. Moseley, D., Reis, A., Parvez, A., et al. 2025. Using variety testing data to select soybean varieties: Guidelines for practitioners. Crop, Forace, & Turfgrass Management. Accepted (Aug 6, 2025). Current status: In production (Sep 5, 2025). 

  2. Poudel, A., Burns, D., Adhikari, R., et al. 2025. Cover crop biomass predictions with Unmanned Aerial Vehicle remote sensing and TensorFlow machine learning. Drones. 9(2), 131.

  3. Acharya, B., Dodla, S., Tubana, B., et al. 2025. Characterizing optimum N rate in waterlogged maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) remote sensing. Agronomy. 15(2), 434.

  4. de Souza, F.L.P., Dias, M.A., Setiyono, T.D., Campos, S., Shiratsuchi, L.S., Tao, H. 2025 Identification of soybean planting gaps using machine learning. Smart Agricultural Technology. 10, 100779.

  5. de Souza, F.L.P., Shiratsuchi, L.S., Dias, M. A., et al. 2025. A neural network approach employed to classify soybean plants using multi-sensor images. Precision Agriculture. 26, 32.

  6. Bampasidou, M. and J. Fields. 2025. “Is Labor Shortage Pushing Towards Automation and Mechanization of the US Nursery Industry?” Choices 40,1 

  7. Bampasidou, M. et al. 2025. Navigating Emerging Technologies in Specialty Crops: Production, Labor and Ethical Considerations. (Theme proposal Choices). Choices 40,1

  8. Hasan, M. R.*, K. P. Paudel, M. Regmi, et al. 2025. “Toward Rice Production Self-Sufficiency in Bangladesh: The Role of Plot Attributes, Farmer Characteristics, and Technology”, Journal of Agricultural and Resource Economics


 


Mississippi State



  1. Gamagedara, Y., Wijewardane, N. K., Feng, G., et al. (2024). Can we use a mid-infrared fine-ground soil spectral library to predict non-fine-ground spectra?. Geoderma, 443, 116799.

  2. Silva, F. H. C. A., Wijewardane, N. K., Cox, et al. (2025). Assessment of different VisNIR and MIR spectroscopic techniques and the potential of calibration transfer between MIR laboratory and portable instruments to estimate soil properties. Soil and Tillage Research, 251, 106555.

  3. H Gharakhani, JA Thomasson, Y Lu, KR Reddy.  (2024). Field test and evaluation of an innovative vision-guided robotic cotton harvester.  Computers and Electronics in Agriculture 225, 109314.  2024.

  4. PK Yadav, JA Thomasson, R Hardin, SW Searcy, U Braga-Neto (2024). AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application. Remote Sensing 16 (15), 2754.


 


NDSU: 



  1. Mathew, F., Kaur, H., George, M., Mohan, K., Mukaila, T., Rafi, N., & Clay, S. Plant disease identification and management using remote sensing. In D. K. Shannon, D. E. Clay, & N. R. Kitchen (Eds.), Precision agriculture basics (2nd ed.). American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc. 


 


OKState:



  1. Vitale, J., & Robinson, J. (2025). In-Season Price Forecasting in Cotton Futures Markets Using ARIMA, Neural Network, and LSTM Machine Learning Models. Journal of Risk and Financial Management, 18(2), 93.

  2. Council for Agricultural Science and Technology. (2024). AI in Agriculture: Opportunities, Challenges, and Recommendations. Issue Paper 75. 


 


Oregon State: 



  1. Weerasekara, M., Hartemink, A.E., Zhang, Y., Stevenson, A., 2024. Spectral signatures of soil horizons and soil orders from Wisconsin. Soil Science Society of America Journal, 1–18.

  2. Ghimire, S., Zhang, Y., Huang, J., et al., 2025. Using mid-infrared spectroscopy to estimate soil microbial properties at the continental scale. Applied Soil Ecology 211, 106110.


 


SDSU:



  1. Wongpiyabovorn, O., Wang, T., Menendez, H., & Yago, A. L. (2025). Precision Livestock Farming Technologies in Beef Cattle Production: Current and Future. Choices, 40(2), 1-8.

  2. Cho, W. & Wang, T. 2025. “From Farm Kids to Ag Tech Leaders: Who’s Driving Precision Agriculture?”. Forthcoming at Choices Magazine.


 


TAMU:



  1. Rahimifar, K. Kaniyamattam, J. Wiegert and L. O. Tedeschi. 2025. A stochastic dynamic model for nutrient requirement and utilization prediction of U.S. based grow-finish swine production systems. J. Ani. Sci. (In Press). (agent-based modeling)

  2. H. A. Samad, R. V. Suhana, K. Vineeth, et al. 2025. An assessment of climate-smart strategies for cleaner, sustainable, and carbon-neutral livestock systems in India. Sustainability, 17(5), 2105.

  3. Tedeschi, L. O., Johnson, D. C., Atzori, A. S., et al. 2024. Applying Systems Thinking to Sustainable Beef Production Management: Modeling-Based Evidence for Enhancing Ecosystem Services. Systems, 12(11), 446.

  4. Pal P., J. Landivar, J. L. Scott, et al. 2025. Unmanned Aerial System and Machine Learning Driven Digital-Twin Framework for Cotton Crop Forecasting. Computer and Electronics in Agriculture, 228, 109589.

  5. Reddy J., Niu H., J. L. Scott, et al. 2024. Cotton yield prediction via UAV-based cotton bolls image segmentation using YOLO and SAM Models. Remote Sensing, 16, 4346.

  6. Khuimphukhieo I., M. Bhandari, J. Enciso, J. A. da Silva. 2025. Estimating sugarcane yield and its components using unmanned aerial systems (UAS)- based High throughput phenotyping (HTP). Computers and Electronics in Agriculture. (in press)

  7. Rose, D. C., Crouch, K., Germundsson, L. B., Gaspard, M., Giller, O., Ortolani, L., & Strong, R. (in-press). Supporting the adoption of digital technology on-farm: ten tips for Extension. Journal of Extension. 

  8. Lee, C-L., & Strong, R. (2025). What factors prevent sustainable agriculture science from being applied?: Understanding U.S. Extension professionals’ intentions to promote precision agriculture technologies. Discover Sustainability, 6(445).           

  9. Mulkerrins, M., Strong, R., Kilboyle, J., et al. (2025). The Influence of Digital Knowledge Exchange on Advancing Irish Students Knowledge and Adoption of Sustainable Grassland Management Innovations. Journal of International Agricultural and Extension Education, 32(1).            

  10. Paudel, D., Kallenberg, M., Ofori-Ampofo, S., et al. (2025). CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting. Earth Systems Science Data.     


 


UArk: 



  1. Pallerla, C., Feng, Y., Owens, C. M., et al. (2024). Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression. Artificial Intelligence in Agriculture.

  2. Bist, R. B., Bist, K., Poudel, S., et al. (2024). Sustainable poultry farming practices: a critical review of current strategies and future prospects. Poultry Science, 104295. 

  3. Li, Z., Wang, D., Zhu, T., Tao, Y., & Ni, C. (2024). Review of deep learning-based methods for non-destructive evaluation of agricultural products. Biosystems Engineering, 245, 56-83. 

  4. Wang, D., Sethu, S., Nathan, S., et al. (2024). Is human perception reliable? Toward illumination robust food freshness prediction from food appearance—Taking lettuce freshness evaluation as an example. Journal of Food Engineering, 112179. 

  5. Xu, Z., Uppuluri, R., Zhang, X., et al. (2025). UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects. IEEE Robotics and Automation Letters. 

  6. Sohrabipour, P., Pallerla, C. K. R., Davar, A., et al. (2025). Cost-Effective Active Laser Scanning System for Depth-Aware Deep-Learning-Based Instance Segmentation in Poultry Processing. AgriEngineering, 7(3), 77. 

  7. Mahmoudi, S., Davar, A., Sohrabipour, et al. (2024). Leveraging Imitation Learning in Agricultural Robotics: A Comprehensive Survey and Comparative Analysis. Frontiers in Robotics and AI, 11, 1441312 (1B)

  8. Crandall, P. G., O’Bryan, C. A., Wang, D., et al. (2024). Environmental monitoring in food manufacturing: Current perspectives and emerging frontiers. Food Control, 110269. 

  9. Tagoe, A., Silva, A., Koparan, C., et al. (2024). Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing. AgriEngineering, 6(4), 4549-4569. 


 


UCDavis:



  1. Karimzadeh, S., Li, Z., & Ahamed, M. S. (2025). Machine learning-based fault detection and diagnosis of electrical conductivity and pH sensors in hydroponic systems. Computers and Electronics in Agriculture, 237, 110544. 

  2. Li, Z., Karimzadeh, S., Chavanapanit, A., et al. (2025). Detection of Calcium Deficiency in Indoor-Grown Lettuce under LED Lighting using Computer Vision. Smart Agricultural Technology, 101144. 


 


MSU:



  1. Xu, J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a new machine vision-based automated sweetpotato grading and sorting system. Journal of the ASABE 67 (5), 1369-1380.

  2. Deng, B., Lu, Y., 2025. Weed image augmentation by ControlNet-added stable diffusion for multi-class weed detection. Computers and Electronics in Agriculture 232, 110123.

  3. Deng, B., Lu, Y., Vander Weide, J., 2025. Development and preliminary evaluation of a YOLO-based fruit counting and maturity evaluation mobile application for blueberries. Applied Engineering in Agriculture 41(3), 391-399.

  4. Deng, B., Lu, Y., Li, Z., 2024. Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9. Smart Agricultural Technology 9, 100620.

  5. Xu, J., Lu, Y., 2025. 3D vision-based perception and length estimation of green asparagus for selective harvesting. Journal of the ASABE 68 (2), 239-256.

  6. Cai, J., Lu, Y., 2025. Detection of woody breast condition in broiler breast fillets using light scattering imaging. Journal of the ASABE 68 (1), 13-24.

  7. Cai, J., Lu, Y., 2025. Assessment of woody breast in broiler breast fillets using structured-illumination reflectance imaging coupled with surface profilometry. Journal of Food Engineering 391, 112459.

  8. Yao, T., Jing, Y., Lu, Y., Liu, W., Lyv, J., Zhang, X., Chang, S., 2024. Recognition of catfish fillets using computer vision toward automated singulation. Journal of Food Process Engineering 47, e14726.

  9. Bhujel, A., Wang, Y., Lu, Y., Morris, D., Dangol, M., 2025. A systematic survey of public computer vision datasets for precision livestock farming. Computers and Electronics in Agriculture 229, 109718.


 


UF:



  1. Vijayakumar V., Ampatzidis Y., Lacerda C., et al., 2025. AI-driven real-time weed detection and robotic smart spraying for optimized performance and operational speed in vegetable production. Biosystems Engineering, 259, 104288.

  2. Zhou C., Ampatzidis Y., Guan H., et al., 2025. Agrosense: Accelerating precision orchard management through an AI-enabled monitoring system. Precision Agriculture, 26(4), 73.

  3. Ma G., Javidan S.M., Ampatzidis Y., Zhang Z., 2025. A novel hybrid technique for detecting and classifying hyperspectral images of tomato fungal diseases based on deep feature extraction and Manhattan distance. Sensors, 25(14), 4285.

  4. Trentin C., Ampatzidis Y., Tasioulas S., Tsouvaltzis P., 2025. Optimizing tomato yield prediction using phenologically timed UAV-based spectral data and machine learning. Smart Agricultural Technology, 101158.

  5. Li X., Huang F., Sun H., et al., 2025. A bio-inspired framework for apple leaf disease detection: integrating lesion localization, ant colony optimization, and machine learning. Smart Agricultural Technology, 101141.

  6. Lacerda C.F., Ampatzidis Y., Neto A.D.C., Partel V., 2025. Cost-efficient high-resolution monitoring for specialty crops using AgI-GAN and AI-driven analytics. Computers and Electronics in Agriculture, 237, 110678.

  7. Kunwar S., Babar A., Jarquin D., Ampatzidis Y., et al., 2025. Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates. The Plant Genome, 18(2), e70052.

  8. McBreen J., Babar A., Jarquin D., et al., 2025. Leveraging multi-omics data with machine learning to predict grain yield in small vs. big plot wheat trials. Agronomy, 15(6), 1315.

  9. Liu S., Ampatzidis Y., Zhou C., Lee W.S., 2025. AI-driven time series analysis for predicting strawberry weekly yields integrating fruit monitoring and weather data for optimized harvest planning. Computers and Electronics in Agriculture, 233, 110212.

  10. Tulu B., Teshome F.T., Ampatzidis Y., et al., 2025. AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture. SoftwareX, 30, 102083.

  11. Javidan S.M., Ampatzidis Y., Banakar A., et al., 2025. An intelligent group learning framework for detecting common tomato diseases using simple and weighted majority voting with deep learning models. AgriEngineering, 7(2), 31.

  12. McBreen J., Babar A., Jarquin D., et al., 2025. Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments. The Plant Genome, 18(1).

  13. Chen, Y., A. Shu, Z. Liu, Y. Chen, et al. 2025. SP-RTSD: A lightweight real-time strawberry detection on edge devices for onboard robotic harvesting. Journal of Field Robotics, 2025; 1-19.

  14. Tapia, R., W.S. Lee, V.M. Whitaker, and S. Lee. 2025. Multiple methods for predicting strawberry powdery mildew severity from field canopy reflectance data. PhytoFrontiers.

  15. Kim, J.-H., Y.-H. Cho, K.-M. Kim, et al. (2025). Sweet potato farming in the USA and South Korea: A comparative study of cultivation pattern and mechanization status. Journal of Biosystems Engineering 50:210–224.

  16. Huang, Z., W. S. Lee, P. Yang, et al. 2025. Advanced canopy size estimation in strawberry production: a machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture 236 (2025), 110501.

  17. Huang, Z., W. S. Lee, P. Zhang, et al. 2025. SASP: segment any strawberry plant, an end-to-end strawberry canopy volume estimation. Smart Agricultural Technology 11 (2025) 101017.

  18. Pardo-Beainy, C., C. Parra, L. Solaque, and W. S. Lee. 2025. Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping. Smart Agricultural Technology 12 (2025) 101293.

  19. Liu, S., Ampatzidis, Y., Zhou, C., & Lee, W. S. (2025). AI-driven time series analysis for predicting strawberry weekly yields integrating fruit monitoring and weather data for optimized harvest planning. Computers and Electronics in Agriculture, 233, 110212.

  20. Hernandez, B., & Medeiros, H. (2024). Multi-object tracking in agricultural applications using a vision transformer for spatial association. Computers and Electronics in Agriculture, 226, 109379.

  21. Medeiros, H., Tabb, A., Stewart, S., & Leskey, T. (2025). Detecting invasive insects using Uncrewed Aerial Vehicles and Variational AutoEncoders. Computers and Electronics in Agriculture, 236, 110362.

  22. Gallios, I.,Tziolas, N., & Tsakiridis, N. (2025). Federated learning applications in soil spectroscopy. Geoderma, 456, 117259.

  23. Kalopesa, E. Tziolas, N., Tsakiridis, N.L., Safanelli, J.L., Hengl, T., Sanderman, J. (2025). Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach. Remote Sensing. 17, 771.  

  24. Zhao, X., Xiong, Z., Karlshöfer, P., et al., (2025). Soil organic carbon estimation using spaceborne hyperspectral composites on a large scale. International Journal of Applied Earth Observation and Geoinformation.

  25. Demattê, J. A. M., Rizzo, R., Rosin, N. A., et al. (2025). A global soil spectral grid based on space sensing. Science of the Total Environment, 968, 178791.

  26. Novais, J. J. M., Melo, B. M. D., Junior, et al. (2025). Online analysis of Amazon's soils through reflectance spectroscopy and cloud computing can support policies and the sustainable development. Journal of Environmental Management, 375, 124155.

  27. Ads, A., Tziolas, N. Al Shehhi, M. R. (2025) Quantitative Analysis of Water, Heat, and Salinity Dynamics During Bare Soil Evaporation. Hydrology.

  28. Demattê, J. A. M., Poppiel, R. R., Novais, et al. (2025). Frontiers in earth observation for global soil properties assessment linked to environmental and socio-economic factors. The Innovation, 6, 100985.


UKY:



  1. Thomasson, J. A., Ampatzidis, Y., Bhandari, M., et al. (2025). AI in Agriculture: Opportunities, Challenges, and Recommendations. Council for Agricultural Science and Technology

  2. Souza, E.F., Fernández, F.G., Fabrizzi, K.P., et al. (2025). Precipitation influences pre‐sidedress soil nitrate thresholds for corn production. Soil Science Society of America Journal, 89(3), p.e70085.

  3. Ragland, J., Egli, D., Mizuta, K., Greb, S., Levy, J.E. (2005). The role of phosphorus in Kentucky Agricultural Development: A story of the haves and the have-nots. University of Kentucky Cooperative Extension Service. ID-278

  4. Ekramirad, N., Doyle, L.E., Loeb, J.R., Santra, D., Adedeji, A.A. (2024). Hyperspectral imaging and machine learning as a nondestructive method for proso millet seed detection and classification. Foods 13(9), 1330.

  5. Tizhe Liberty, J., Sun, S., Kucha, C., Adedeji, A. A., Agidi, G., & Ngadi, M. O. (2024). Augmented reality for food quality assessment: bridging the physical and digital worlds. Journal of Food Engineering 367, 111893.

  6. Anand, L., Combs, D. (2025). “chromoMap: A tool for interactive visualization if multi-omics data and annotation of chromosomes”. (Innovation Report Application: OI2025-00810). Disclosure date May 25, 2025

  7. Rodríguez López C.M. (2025). “A Non-Invasive Method To Predict Gestational Conditions In Pregnant Mares”. (Provisional Patent Application: WO2011157995). Filed April 3, 2025


 


ALL STATION CONFERENCE PRESENTATIONS: PODIUM/POSTER


KSU:



  1. Deepak R. Joshi, David Clay, Sharon Clay and Prakriti Sharma. Multisource Data Fusion and Machine Learning for Accurate on-Farm Corn Yield Prediction.  ASA, CSSA and SSSA International Annual Meeting.  November 9-12, 2024, San Antonio, TX.   

  2. Jitendar Rathore, Deepak R. Joshi, Halimeh Abuayyash, et al. Co-designing a Zone-Specific On-farm Digital Support System for Crop Yield Prediction. AGU International Conference, December 9-13, 2024, Washington, D.C. 

  3. Jitendar Rathore, Deepak R. Joshi, et al. Employing random forest, support vector machine learning models, and Planet Scope satellite data to predict crop yield on the farm. AGU International Conference, December 9-13, 2024, Washington, D.C. 

  4. Tulsi P. Kharel*, Heather L. Tyler, Partson Mubvumba, et al., Mixed Species Cover Crop and Nutrient Yield Estimation Using Multi-Spectral Drone Imagery. ASA, CSSA and SSSA International Annual Meeting.   November 9-12, 2024, San Antonio, TX. 

  5. Dua, S., B. Aryal, J. Peiretti, A. Sharda. (2025) Optimization of GWL margin to enhance planter performance. 2025 ASABE Annual International Meeting 2501079. 

  6. Kaloya, T., A. Sharda. (2025) Quantifying Horizontal and Vertical Movement Accuracy in Agricultural Sprayer Booms Using a Distance Quantifier System Integrated with GPS and CAN Networks. 2025 ASABE Annual International Meeting. 2501029

  7. Janbazialamdari, S., E. Brokesh.  (2025) Enhancing Soil Compaction Prediction in Precision Agriculture Using Advanced Machine Learning Models. 2025 ASABE Annual International Meeting. 2501409

  8. Singh, R.,  A. Sharda (2025) A Comprehensive Assessment of Fertilizer Response Across Diverse Nutrient Application Strategies for Enhanced Crop Vigor and Yield. 2025 ASABE Annual International Meeting. 2500460 

  9. Sharda, A., J. Peiretti, (2025).  Assessing Toolbar Location's Impact on Autonomous Regulation in Row Crop Planters. 2025 ASABE Annual International Meeting. 2501642 

  10. Vail, B., A. Sharda, B. McCornack. (2025) Development and Testing of a Seed Placement Data Collection System for Wheat Drill Row Units Using Computer Vision. 2025 ASABE Annual International Meeting.  2501558 

  11. Dua, A., A. Sharda, W. Schapaugh, R. Hessel. (2025) Automated tool for rapid data analytics of remotely sensed data for phenotypic and precision agriculture applications. 2025 ASABE Annual International Meeting. 2500879 

  12. Dua, A., A. Sharda, W. Schapaugh, R. Hessel. (2025) Ry Sense: An Automated Tool for Rapid Yield Predictions of UAV based Remotely Sensed Data for Field Based Breeding Programs. 2025 ASABE Annual International Meeting. 2500879

  13. Deepak Joshi. How AI and Precision Agriculture Enhance Farm Decision-Making and Efficiency. Sustainable Agronomy Conference 2025, July 9-30, 2025 (Virtual), Organized by American Society of Agronomy.  

  14. Deepak Joshi. Utilization of Different Data Layers for Precision Decision Making in On-farm Research. Data Tech Conference 2025, May 16, 2025, Minneapolis, MN, Organized by MinneAnalytics.  

  15. Deepak Joshi. Spray Drone in Pasture and Ranch Management. AI in Kansas Agriculture Conference, July 22, 2025, Lyndon, KS 

  16. Deepak Joshi. Management Zones & Variable Rate Technology in Precision Agriculture. Kansas Agricultural Technology Conference, March 7, 2025, Clay Center, KS, organized by Kansas Agricultural Research and Technology Association (KARTA). 

  17. Deepak Joshi. Utilization of Different Data Layers for Precision Decision Making in On-farm Research. K-State Plant Pathology Department Seminar, January 30, 2025, in Manhattan, KS.

  18. Sharda, Ajay. Future of AI and Autonomy. Lincoln Agritech at Lincoln University, Lincoln, New Zealand. Feb 17th, 2025.

  19. Sharda, Ajay.  AI and Autonomy in Ag. AI in Ag Conference. Mississippi State University. April 1st, 2025  

  20. Sharda, Ajay. Computer Vision, Machine Learning and AI system integration for air-seeder furrow quality quantification. ECPA 2025 Barcelona, Spain. July 1, 2025.


 


LSU:



  1. Setiyono, T., Gentimis, T., Rontani, F., et al. 2025 Application of Tensor Flow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA 

  2. Pokharel, R., Poudel, A., Sutthanonkul, T., et al. 2025. AI-Driven rice yield prediction using UAV imagery and plant parameters across growth stages. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 

  3. Sutthanonkul, T., Gentimis, T., Kimbeng, C., Setiyono, T. 2025. The predictive modeling of sugarcane yield utilizing Artificial Intelligence (AI) techniques while fusing phenotypic, genotypic, and Unmanned Aerial Vehicle (UAV) remote sensing data. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 

  4. Poudel, A., Burns, D., Adhikari, R., et al. 2025. Cover crops biomass predictions with UAV remote sensing and TensorFlow machine learning. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 


 


NDSU:



  1. Diwakar, P., Karg, Z., Paxton, H., et al. 2025. Multi-modal sensing for precision agriculture: LIBS, spectral imaging, and machine learning for soil and plant health monitoring. SciX Conference 2025, Covington, KY. October 5-10, 2025. (Talk) 


 


OKState: 



  1. Coalition for Advancing Digital Research & Education June 25, 2025 — Oklahoma State University, Stillwater, OK
    Symposium Theme: Ethical Application and Regulation of Artificial Intelligence in Research and Education Presented: “Regenerative AI Bots in Teaching: Immersive Modules for Ag Econ Classrooms”


 


 


Oregon State:



  1. Qi, M., Weerasekara, M., Zhang, et al., 2025. Predicting soil carbon fractions and sequestration potential using mid-infrared spectroscopy. Oregon Society of Soi Scientists Annual meeting. Poster 

  2. Kalisz, A., Zhang, Y., Brungard, C., Maynard, J., Hodges, R., 2025. SpectrAnd: Spectroscopy for rapid identification of Andic soil properties. Oregon Society of Soi Scientists Annual meeting.

  3. Weerasekara, M., Zhang, Y., Hartemink, A.E., Maynard, J., 2025. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. Oregon Society of Soi Scientists Annual meeting. Poster 

  4. Zhang, Y., Weerasekara, M., Hartemink, A.E., Maynard, J., 2024. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. American Geophysical Union Fall Meeting. Poster 

  5. Weerasekara, M., Zhang, Y., Hartemink, A.E., Maynard, J., 2024. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. ASA-CSSA-SSSA Annual Meeting. Oral 

  6. Zhang, Y., 2024. Applications of big data and machine learning in soil science: what do we need from domain science in the era of artificial intelligence? Fall 2024 UK Artificial Intelligence and Machine Learning Research Symposium 


 


 


SDSU:



  1. Wongpiyabovorn, O., Wang, T., Menendez, H., & Yago, A. L. (2025). Precision Livestock Farming Technologies in Beef Cattle Production: Current and Future. Choices, 40(2), 1-8.

  2. Cho, W. & Wang, T. 2025. “From Farm Kids to Ag Tech Leaders: Who’s Driving Precision Agriculture?”. Forthcoming at Choices Magazine.

  3. Wang, T., H. Jin., & A. Oyebanji. “Factors Affecting Farmer Adoption of Unmanned Aerial Vehicles: Current and Future.” Revised and Resubmitted to Precision Agriculture.

  4. Han G., Z. Wei, T. Wang, “Adoption of Precision Agriculture Technology Bundles: Role of Values and Perceptions.” Submitted to Technology in Society. 


 


 


TAMU:



  1. Wang, Y., Xu, Z., 2025. Evaluate changes in respiratory rate of lateral lying sows around onset of parturition using depth camera. Oral presentation at the USPLF Conference (Lincoln, NE) 

  2. Wang, Y., Xu, Z., 2025. Investigation of poultry fecal removal efficiency and volume estimation on grooved-floor panels. Oral presentation at the Poultry Science Association (Raleigh, NC) 

  3. Zhao, Z., Xu, Z., 2025. Evaluation of mild woody breast meat’s contact stiffness via beam buckling mechanism. Oral presentation at the Poultry Science Association (Raleigh, NC) 

  4. Strong, R., Herbert, B., Bhandari, M.,  et al. (2025, July). ExtensionBot’s AI Impacts on Extension and Advisory Services [Refereed Oral Presentation]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 

  5. Strong, R., Athanasiadis, I., & AgML Community. (2025, July). The Emergence of AgML’s CY-Bench: An AI Platform to Enhance Engagement, Empowerment, and Partnerships [Refereed Oral Presentation]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 

  6. Strong, R., & Landaverde, R. (2025, June). Stargate and academic opportunities to elevate AI food system knowledge transfer [Refereed Oral Presentation]. Development Studies Association conference. University of Bath; Bath, United Kingdom. 

  7. Marburger, M., Strong, R., Fares, A., et al. (2025, April). SDG Project Impacts from Water Instructional Interventions: National Competitive Funds Develop the Next Generation of Climate Smart Agriculture Leaders [Refereed Oral Presentation]. Association for International Agricultural Extension and Education conference. Kingsmills Hotel and Conference Center; Inverness, Scotland. 

  8. Dooley, K., Strong, R., Fares, A., Moore, J., & Marburger, M. (2025, April). Harnessing AKIS to Understand Multi-disciplinary Multi-institutional Faculty’s Climate Smart Knowledge Transfer Impact [Refereed Poster Presentation]. Association for International Agricultural Extension and Education conference. Kingsmills; Inverness, Scotland. 

  9. Marburger, M., Strong, R., Murray, S., Fares, A., & Porter, A. (2025, April). Are We Enhancing AI Knowledge Transfer and Fostering Collaboration?: 2024 AI in Agriculture and Natural Resources Conference Inferential Data Impacts [Refereed Oral Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 

  10. Ahn, J., Benge, M., Greenhaw, L., & Strong, R. (2025, April). AI and Social Networks in Florida’s Agricultural Extension Systems [Refereed Oral Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 

  11. Marburger, M., Kaniyamattam, K., Tedeschi, L., Strong, R., & Reedy, D. (2025, April). Student Responses and Large Language Models: Artificial Intelligence’s Attributes in Understanding Animal Nutrition and Human Health Research [Refereed Poster Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 

  12. Artificial Intelligence-Driven Stocker Economics Visualization Decision Support Tool for Sustainable Beef Production (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)

  13. Machine Learning for Economic Decision-Making in Texas Stocker Cattle Operations (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)

  14. Causal Relationships of Ecosystem Services: A Systems Approach for Diversifying Income in Cow-Calf Operations (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)

  15. Strong, R. (July, 2025). AI tools and impacts for farmers [Invited Seminar]. Northeast SARE (Sustainable Agriculture Research and Education) Virtual PDP Workshop. Cornell University; Cornell, New York. 

  16. Strong, R. (July, 2025). Student knowledge and career goal impacts from participation in NIFA funded smart agriculture project [Invited Seminar]. Prairie View A&M University; Prairie View, Texas.

  17. Strong, R. (July, 2025). Digitalization and artificial intelligence in education and Extension: Lessons learned and ethical considerations [Panelist]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 

  18. Strong, R., Palcynski, L. & Landaverde, R. (2025, June). Artificial intelligence opportunities for developing transformative positive change in future food systems. [Invited Leaders for Seminar Panel]. Development Studies Association conference. University of Bath; Bath United Kingdom.

  19. Jurney, C., Paez, X., Haynes, M., Wall, E., & Strong, R. (2025, June). Use of AI to enhance efficiency of administration [Invited Presentation]. LEAD AgriLife. Plaza Hotel; San Antonio, Texas. 

  20. Strong, R. (2025, May). AI tools and impacts for agricultural service providers [Invited Seminar]. Northeast SARE (Sustainable Agriculture Research and Education) Virtual PDP Workshop. Cornell University; Cornell, New York.

  21. Strong, R., & Marburger, M. (2024, December). Social implications effecting smart agriculture knowledge transfer to stakeholders [Invited Presentation]. Smart Agriculture Workshop. Texas A&M University; College Station, Texas. 


 


UArk:



  1. Pallerla C., Subbiah J., Bist R., et al. (2025) Enhancing foreign material detection in poultry processing plants using thermal imaging and vision-based systems. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Oral]. 

  2. Pallerla C., …, Wang D.#, (2024) Hyperspectral imaging and Machine learning algorithms for foreign material detection on the chicken surface. In 2024 Poultry Science Annual International Meeting. Lexington, KY [Oral] 

  3. Atungulu G., Tachine C., Pallerla C., Wang D. (2025) Assessment of a New Nondestructive Method to Measure Rice Chalk Content Based on Rough Rice Properties. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Oral] 

  4. Vinson S., Wang D. (2025) Cross-Facility Reliable Deep Learning Based Beef Marbling Assessment Via Unsupervised Domain Adaptation Regression. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Poster, proceeding] 

  5. Feng Y., Pallerla C., Lin X., et al. (2025) Leveraging Blender-Synthesized Data and Depth information for High-Precision Instance Segmentation of Chicken Carcasses. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Poster] 

  6. Davar A., Xu Z., Mahmoudi S., et al. (2025) ChicGrasp: Imitation-Learning Control for Dual-Finger Manipulation of Delicate, Irregular, and Bio-products. In International Conference on Robotics and Automation (ICRA) 2025[Poster] 

  7. (1B)Mahmoudi S., Wang D.#, (2025) Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing   In International Conference on Robotics and Automation (ICRA) 2025 [Poster] 

  8. Wang, D., Mahmoudi S., Griscorn C., Crandall P (2024). Automated Environmental Swabbing: A Robotic Solution for Enhancing Food Safety in Poultry Processing — Human Swabbing Evaluation and Preliminary Robotic Swabbing Setup. In 2024 Institute of Biological Engineering (IBE) Annual Meeting, Atlanta, GA [Oral]

  9. Azmir, M. N., Tagoe, A., Koparan, C., et al. (2025). Automated Weed Pressure Measurement System Evaluation for Unmanned Aerial Vehicles. In 2025 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. [Poster, proceeding] 

  10. Koparan C., Johnson D., Wang D., Worthington M., Poncet A., (2025) Preliminary Analysis of Computer Vision for Blackberry Flower Quantification. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting.Toronto, Canada [Poster] 


 


UCDavis:



  1. Li, Z., & Ahamed, M. S. Advancing Lettuce Growth Modeling in Controlled Environments with Physics-Informed Neural Networks. ASABE Annual Meeting 2025, Toronto, Canada. 

  2. Karimzadeh, S. & Ahamed, M. S. GrowDose: A Novel Software for Precision Ion-Based Nutrient Management in Closed-Loop Hydroponics. ASABE Annual Meeting 2025, Toronto, Canada.

  3. Karimzadeh & Ahamed, M. S. Advanced Model Predictive Control for Optimized Nutrient Management in Closed-Loop Hydroponics. ASABE Annual Meeting 2025, Toronto, Canada.


 


MSU:



  1. Xu, J., Lu, Y., 2025. Development and evaluation of a multispectral vision-based automated sweet potato sorting system. Sensing for Agriculture and Food Quality and Safety XVII.

  2. Deng, B., Lu, Y., 2025. Weed image augmentation by IP-adapter-based stable diffusion for multiclass weed detection. Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III; 1345909.

  3. Deng, B., Lu, Y., Brainard, Improvements and Evaluation of a Smart Sprayer Prototype for Weed Control in Vegetable Crops. 2025 ASABE Annual International Meeting 2500323

  4. Deng, B., Lu, Y., Brainard, D., 2025. Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments. https://doi.org/10.48550/arXiv.2502.17673

  5. Singh, N., Lu, Y., 2025. Development and Laboratory Assessment of Cutting and Snapping Mechanisms for Green Asparagus Harvesting. 2025 ASABE Annual International Meeting 2500323.

  6. X. Yang, A. Bhujel, M. Bashar, M. Benjamin, D. Morris, 2025. Enhanced Piglets Monitoring with a Multiview Camera System” in 2025 ASABE Annual International Meeting.

  7. B. Smith, Y. Long, D. Morris, 2025. An Automated LED Intervention System for Poultry Piling, 2025 ASABE Annual International Meeting.

  8. A. Bhujel, D. Morris, J. Siegford, M. Benjamin, M. Bashar ‘A Computer Vision Dataset for Monitoring and Tracking Gilt’s Daily Activities” in 3rd US Precision Livestock Farming Conference, 2025.


 


UF:



  1. Zhou L., Amini M., Reisi Gahrooei M., Ampatzidis Y., 2025. Integrating federated learning and hyperspectral imaging for early detection of tomato disease. MIT URTC (Undergraduate Research and Technology Conference), Cambridge, Massachusetts, October 10-12, 2025.

  2. Ampatzidis Y., 2025. Harvesting Innovation Together: Cross-Sector Synergies in Agricultural Robotics. Industry-Academia Panel. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.

  3. Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-powered autonomous smart sprayer for precision weed management: Advancing sustainable agriculture through machine vision, automation, and control systems. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.

  4. Frederick Q., Burks T., Watson J., et al., 2025. Investigating feature types for automated citrus peel disease detection. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.

  5. Liu W., Ampatzidis Y., Wilkinson B., 2025. The accuracy of remote sensing technologies for tree height estimation: a comparative evaluation in citrus orchards. ASABE Annual International Meeting, Toronto, Ontario, Canada, July 13-16, 2025, 2500561, doi:10.13031/aim.202500561.

  6. Liu W. and Ampatzidis Y., 2025. Optimizing citrus tree detection: A novel improved distance-based individual tree segmentation method for aerial LiDAR data. ASABE Annual International Meeting, Toronto, Ontario, Canada, July 13-16, 2025.

  7. Cho Y., Yu Z., and Ampatzidis Y., 2025. Enhancing trust in agriculture: Addressing data sharing with blockchain technology. ASABE Annual International Meeting, Technical Session 110 – Connectivity, Cloud Computing, and Internet of Things in Agriculture and Natural Resources, Toronto, Ontario, Canada, July 13-16, 2025.

  8. Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-driven autonomous spraying for precision weed management in specialty crop production. 15th European Conference on Precision Agriculture, Barcelona, Spain, June 29 – July 3, 2025.

  9. Liu W. and Ampatzidis Y., 2025. Enhancing canopy height measurement accuracy and efficiency in citrus orchards with LiDAR. Florida State Horticultural Society (FSHS) Annual Conference, Bonita Springs, Florida, June 8-10, 2025.

  10. Pullock D.A., Zhou C., Ampatzidis Y., et al., 2025. Potential for automation of citrus psyllid pest identification using computer vision-based artificial intelligence recognition. 2nd International Electronic Conference on Entomology, Online, May 19-21, 2025.

  11. Ampatzidis Y., Liu S., Guan H., Neto A.D.C., 2025. Enhanced AI-driven sensing and analytics platform for precision orchard management. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X Conference, SPIE Defense + Commercial Sensing, Orlando, FL, April 14-16, 2025.

  12. Guan H., Neto A.D.C., Liu S., Ampatzidis Y., 2025. AI-driven precision spraying with canopy-specific parameter optimization for enhanced orchard efficiency. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.

  13. Huang, Z., and W. S. Lee. 2025. A state space model with tree topology for strawberry detection. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.

  14. Liu S., Ampatzidis Y., Lee W.S., Zhou C., 2025. Optimizing strawberry harvest planning through machine vision and AI-enabled predictive analytics. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.

  15. Huang, Z., W.S. Lee, and M. Le. 2025. AI-driven plant tracking and segmentation for precise canopy estimation in strawberry field. ASABE Paper No. 202500347. St. Joseph, MI.: ASABE.

  16. Daryani, A. E., Bhutta, M., Hernandez, B., & Medeiros, H. (2025). CaMuViD: Calibration-Free Multi-View Detection. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 1220-1229).

  17. Khademi, Z. and Medeiros, H. (2025). End-to-end multi-object tracking and segmentation for precision agriculture. In American Society of Agricultural and Biological Engineers Annual International Meeting.

  18. Daryani, A. E., and Medeiros, H. (2025). AgriTrack: A Robust Framework for Temporal Tracking of Lettuce Plants. In American Society of Agricultural and Biological Engineers Annual International Meeting.

  19. Wang, R., Hofstetter, D., Medeiros, H., and Boney, J., 2025. YOLO + Focal Loss to Improve Detection of Turkey Behaviors. Agricultural and Biological Engineering (ABE) Poster Symposium, University of Florida, Gainesville, FL, March 26, 2025.

  20. Khademi, Z. and Medeiros, H. (2025). Multi-Object Tracking and Segmentation for Precision Agriculture. American Society of Agricultural and Biological Engineers Florida Section Meeting.

  21. H. Medeiros (2025), Robotic Perception for Agricultural Applications​: How advanced models are enabling novel robotic platforms for agricultural production, EPSRC Centre for Doctoral Training in Agri-Food Robotics Online Conference (invited talk).

  22. H. Medeiros (2025). Artificial Intelligence and Robotics in Agriculture​: How AI & robotics are impacting agriculture and food production​. Strategic Dialogues for the Future: Advancing Agricultural Science to Deliver Societal Value in LAC and the U.S. FONTAGRO Workshop (invited talk).


 


UKY:



  1. Rodríguez López C.M. Development of an Epigenetic Clock to Predict Gestational Age in pregnant mares. Commonwealth Computational Summit 2024, Lexington, KY, 10/16/2024

  2. Rodríguez López C.M. Panelist: AI in Agriculture: Opportunities, Challenges, and Recommendations. 4thannual AI in Agriculture conference Starkville, MS. April 1-3, 2025.

  3. Chen, S., Rodríguez López C.M. MacLeod, J. White Blood Cell Population Flux during Gestation in Pregnant Mares Annual Kentucky American Water Science Fair (1st place animal science category) – Central Kentucky Regional Science and Engineering Fair (3rd place animal science category).

  4. Khalsa, S.J.; Mizuta, K.; Nagel, P. (2025) Developing a Standard for Validation of Innovative Methods in Agricultural Soil Testing. European Geosciences Union. Vienna, Austria

  5. Mizuta K.  (2024) From Soil to Success: Leveraging Proximal and Remote Sensing Technologies with AI for Precision Tobacco Agriculture, KY Tobacco Research and Development Center. Lexington, KY.

  6. Mizuta K.  (2024) Collaboration Opportunities: Precision Agriculture, Pedometrics, AI, and Soil Health, The UK Research and Education Center in Princeton. Princeton, KY.

  7. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data. 16th International Conference on Precision Agriculture, Manhattan, KS.

  8. Mizuta K. (2024) Challenges and Opportunities in Sensor-Based Site-Specific Soil Survey Research and Management. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX.

  9. Mizuta K. (2024) Toward Efficient, Profitable, and Sustainable Food Production Systems and Better Environmental Outcomes Through Data-Driven Computational Approaches. University of Kentucky Department of Biosystems and Agricultural Engineering.

  10. Mizuta K. (2024) Perspectives on Soil Health Research in the U.S.: Definitions, Key Players, Grants, Quantitative Measurements, and Other Institutional Activities. The Science Council of Japan.

  11. Mizuta K., Nagel, P., Zamudio, W., Paliotti, M., and Clingensmith, C. (2024) Comparing the Prediction Accuracies of Machine Learning and Deep Learning-Based Models for Extractable Phosphorus in Soil for Precision Agriculture Purposes. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX.

  12. Mizuta K., Zamudio, W., and Nagel, P. (2024) In-Situ Soil Spectroscopy Application for Extractable Phosphorus Prediction for Precision Agriculture with Machine and Deep Learning Models. Pedometrics International Conference. Las Cruces, NM.

  13. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data. 16th International Conference on Precision Agriculture, Manhattan, KS.

  14. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Trial Data: A Case Study for Nitrogen Trials. International Conference for On-Farm Precision Experimentation. South Padre Island, TX.

  15. Miao Y, Kechchour A, Sharma V, Flores A, Lacerda L, Mizuta K, Lu J, and Huang Y.  (2024) In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote Sensing. 16th International Conference on Precision Agriculture, Manhattan, KS.

  16. Kechchour A, Miao Y, Folle S, and Mizuta K. (2024) On-farm Evaluation of The Potential Benefits of Variable Rate Seeding for Corn in Minnesota. 16th International Conference on Precision Agriculture, Manhattan, KS.

  17. Morales-Ona A, Quinn D, Mizuta K, Miao Y. (2024) Effects of Crop Rotation on Estimation of In-Season SidedressNitrogen Rates for Corn Based on Satellite Imagery. 16th International Conference on Precision Agriculture, Manhattan, KS.

  18. Morales-Ona A, Quinn D, Mizuta K, Miao Y. (2024)  Precision Nitrogen Management. Lessons learned from our On-farm PNM project (2021-2023). 75th Annual Corn Improvement Conference, West Lafayette, IN.

  19. Lu J, Miao Y, Mizuta K, et al. (2024) On-farm Evaluation of a Remote Sensing-based Precision Nitrogen Management Strategy Across Diverse Corn Trials. 16th International Conference on Precision Agriculture, Manhattan, KS.

  20. Negrini R, Miao Y, Mizuta K, et al. (2024) Within-Field Spatial Variability in Optimal Sulfur Rates For Corn (Zea mays L.) in Minnesota: Implications for Precision Sulfur Management. 16th International Conference on Precision Agriculture, Manhattan, KS.

  21. Negrini R, Miao Y, Mizuta K (2024) Optimizing Sulfur Management in Corn through On-Farm Experimentation and Machine Learning in Minnesota: A Study on Within-Field Variability and Limiting Factors. ASABE Regional Meeting, Brookings, SD.

  22. Oloyede, A. and Adedeji, A.A. (2025). Deep learning-based hyperspectral model reconstruction from RGB data for gluten detection and quantification in foods. A paper presented (oral) during the Annual International Meeting of American Society of Agricultural and Biological (ASABE) held at the Sheraton Centre Hotel, Toronto, Canada from July 13 – 16, 2025. Paper #: 2500068.

  23. Oloyede, A. and Adedeji, A.A. (2025). Development of a multispectral real-time system for gluten detection and quantification in gluten-free products. A paper presented (poster) during the Annual International Meeting of American Society of Agricultural and Biological (ASABE) held at the Sheraton Centre Hotel, Toronto, Canada from July 13 – 16, 2025.  Paper #: 2500067

  24. Khalsa, S.J.; Mizuta, K.; Nagel, P. (2025) Developing a Standard for Validation of Innovative Methods in Agricultural Soil Testing. European Geosciences Union. Vienna, Austria

  25. Nagel, P, Mizuta, K., and Khalsa, S.J. (2024) Towards a Standardized Protocol and Policy for Acceptance of Innovative Soil Testing. 2024 IEEE India Geoscience and Remote Sensing Symposium. Goa, India, Dec 2-5, 2024.


 


 


EXTENSION TRAINING AND CONFERENCES FACILITATED



  1. KSU: Sharda, Ajay.  Co-Organized the AI in Kansas Ag Conference in Lyndon, KS July 22, 2025.  This conference was organized through the ID3A group and Kansas State Research and Extension.  The conference was part of a series of AI in Ag conferences presented this year.

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