OLD S1069: Research and Extension for Unmanned Aircraft Systems (UAS) Applications in U.S. Agriculture and Natural Resources

(Multistate Research Project)

Status: Inactive/Terminating

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Annual Reports

07/27/2022

06/21/2023

Publications:



  1. Kassim, Y.B.; Oteng-Frimpong, R.; Puozaa, D.K.; Sie, E.K.; Abdul Rasheed, M.; Abdul Rashid, I.; Danquah, A.; Akogo, D.A.; Rhoads, J.; Hoisington, D.; Burow, M.D., and Balota, M. 2022. High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana. Agronomy (MDPI), 12, 2733.

  2. Sarkar, S., Oakes, J., Cazenave, A.B., Burow, M.D., Bennett, R.S., Chamberlin, K. D., Wang, N., White, M., Payton, P., Mahan, J., Chagoya, J., Sung, C-J., McCall, D.S., Thomason, T.E., and Balota, M. 2022. Evaluation of the U.S. peanut germplasm mini-core collection in the Virginia-Carolina region using traditional and high-throughput methods. Agronomy (MDPI) 12(8), 1945

  3. Chapu, I., Okello, D.K., Okello, R.C.O., Odong, T.L., Sarkar, S., and Balota, M. 2022. Exploration of alternative approaches to phenotyping of late leaf spot and groundnut rosette virus disease for groundnut breeding. Front. Plant Sci., 13: 912332, doi: 10.3389/fpls.2022.912332. (915 views, 7/21/2022).

  4. Sie, E.K., Oteng-Frimpong, R., Kassim, Y.B., Puozaa, D.K., Adjebeng-Danquah, J., Masawudu, A.R., Ofori, K., Danquah, A., Cazenave, A.B., Hoisington, D., Rhoads J., and Balota, M. 2022. RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa, Front. Plant Sci., 13:901561

  5. Molaei, B., A. K. Chandel, R. Troy Peters, R. Khot, A. Khan, F. Maureira, and C. Stockle. 2023. Investigating the application of artificial hot and cold reference surfaces for improved ETc estimation using the UAS-METRIC energy balance model. Agricultural Water Management, 284, 108346. https://doi.org/10.1016/j.agwat.2023.108346.

  6. Chandel, A.K., A.P. Rathnayake, and R. Khot. 2022. Mapping apple canopy attributes using aerial multispectral imagery for precision crop inputs management. Acta Horticulturae. 1346, 537-546, https://10.17660/ActaHortic.2022.1346.68

  7. Molaei, B., A. Chandel, R.T. Peters*, R. Khot, and J.Q. Vargas.  2022.  Investigating lodging in Spearmint with overhead sprinklers compared to drag hoses using the texture feature from low altitude RGB imagery. Information Processing in Agriculture, 9(2): 335-341 https://doi.org/10.1016/j.inpa.2021.02.003

  8. Molaei, B., R.T. Peters*, R. Khot, and C. Stockle. 2022. Assessing suitability of auto-selection of hot and cold anchor pixels of the UAS-metric model for developing crop water use maps. Remote Sensing, 14(18), 4454; https://doi.org/10.3390/rs14184454

  9. Chandel, A. K., Amogi, B., Khot, L., Stockle, C. O., and R. T. Peters. 2022. Digitizing Crop Water Use with Data-Driven Approaches. Resource Magazine, 29(4), 14--16.

  10. Amogi, B., J. Schrader, Khot, and G.-A. Hoheisel. 2023. Decision support tools for frost mitigation. Washington State University – Fruit Matters, March 2023. https://treefruit.wsu.edu/article/frost-mitigation/

  11. Sharma, P., Leigh, L., Chang, J., Maimaitijiang, M., & Caffé, M. (2022). Above-ground biomass estimation in oats using UAV remote sensing and machine learning. Sensors, 22(2), 601.

  12. Dilmurat, K., Sagan, V., Maimaitijiang, M., Moose, S., & Fritschi, F. B. (2022). Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data. Remote Sensing, 14(19), 4786.

  13. Maimaitijiang M., Millett, B., Paheding S., Khan SN., Dilmurat K., Reyes A., Kovács P. (2023). Estimating crop grain yield and seed composition using deep learning from UAV multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

  14. Khan SN., Maimaitijiang M., Millett, B., Paheding S., Li DP., Caffé M., Kovács P. (2023). Simultaneously estimating crop yield and seed composition using multitask learning from UAV multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium IGARSS.


Technical Presentations/Abstracts



  1. Cann, M. D., B. R. Amogi, S. Gorthi, G. A. Hoheisel, and L. R. Khot. 2022. Observing and Forecasting Near-Surface Temperature Inversions for Effective frost Mitigation in Central WA Perennial Specialty Crops. American Meteorological Society 20th Conference on Mountain Meteorology, June 27 - July 2, 2022. (Oral Presentation)  https://ams.confex.com/ams/20MOUNTAIN/meetingapp.cgi/Paper/402770 

  2. Chandel, A.K., L.R. Khot, V. Blanco, L. Kalcsits, N.E. Kilham, and S. Mantle. 2022. Multi-scale remote sensing data driven water use and stress mapping of apple trees. Paper No. 2201120 (Oral Presentation), ASABE Annual International Meeting 2022, Houston, Texas, USA, July 17-20, 2022.

  3. Molaei, B., Peters, R.T., Chandel, A.K., Khot, L.R., Stockle, C.O., 2022. Investigating practical artificial hot and cold reference surfaces for improved ET estimation using UAS-METRIC energy balance model. Paper No. 2200629 (Oral Presentation). ASABE Annual International Meeting 2022, Houston, Texas, USA, July 17-20, 2022 (Awarded as best oral presentation paper).



  1. Chandel, A.K., Khot, L.R., Peters, T.R., Stöckle, C.O., Mantle, S., Kalcsits, L., Blanco, V., Kilham, N.E., 2022. Multi-scale remote sensing and energy balance modeling for geospatial evapotranspiration mapping of apple trees. Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture. Auburn University, AL, USA, March 9-11, 2022.

  2. Amogi, B. R., M. D. Cann, G. S. Kothawade, S. R. Gorthi, K. Yumnam, G.-A. Hoheisel, and L. R. Khot. 2022. Drone based temperature inversion profile mapping for understanding the effectiveness of wind machine-based frost mitigation in fruit crops. Paper No. 2201213, ASABE 2022 Annual International Meeting, Houston, TX, July 17-20, 2022 (Oral Presentation).

  3. Schrader, M.J., R. K. Sahni, and L. R. Khot. 2022. Row-aligned vs. cross-row low-altitude aerial spray applications in modern vineyards. Poster presentation, Washington State Grapes Society (WSGS) Annual Meeting, Grandview, WA. November 17-18, 2022. Participants: ~ 75.


Extension Outreach talks



  1. (Invited) Talk on “Digital agriculture and drones”, Spokane Ag Expo and Pacific NW Farm Forum, Spokane, WA. February 7, 2023. Time: 60 min. Participants: ~40.

  2. Talk and discussion on “Smart orchard learnings from 3 years”, Columbia Basic Tree Fruit Club Meeting, Kennewick, WA. January 24, 2023. Time: 30 min. Participants: ~15.

  3. Talk on “Smart orchard: Tools to monitor crop water use”, 76th Annual Lake Chelan Horticultural Meeting, Chelan WA. January 21, 2023. Time: 30 min. Participants: ~95.

  4. (Invited) Talk on “Crop protection technologies for modern orchard systems”, New Frontiers Conference on Targeted Solutions in Crop Protection for Sustainable Agriculture, Corteva Campus, Indianapolis, IN. October 13, 2022. Time: 30 min. Participants: 80 (combined in-person & virtual).

  5. (Invited) Talk on “Drones & Washington Agriculture”, AUVSI Cascade Symposium on Drones, Droids, and Uncrewed systems, Walla Walla, WA. May 25, 2022. Time: 30 min, Participants: ~55.


Teaching


                BYSE 551: UAS in Ag (2 credits) Spring 2023


Datasets:


Killian, E., Lachowiec, J., Sherman, J., Lutgen G., Eberly, J. 2023. High resolution aerial imagery of barley over a growing season. Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kdhp

08/29/2024

Scientific/Research Publications



  1. Jjagwe, P., Chandel, A.K., Langston, D., 2023. Pre-harvest corn grain moisture estimation using aerial multispectral imagery and machine learning techniques. Land, 12(12), p.2188. https://doi.org/10.3390/land12122188

  2. Jjagwe, P., Chandel, A.K., Langston, D., 2023. Soybean yield and vigor assessment against nematode infestation using SUAS-based aerial multispectral imagery and machine learning. Remote Sensing, Under Review.

  3. Prior, E.M., N. Michaelson, J.A. Czuba, T.J. Pingel, V.A. Thomas, and W.C. Hession (in review), Lidar DEM and computational mesh grid resolutions modify roughness in 2D hydrodynamic models, Water Resource Research.

  4. Resop, J.P., C. Hendrix, T. Wynn-Thompson, and W.C. Hession (Accepted, in press), Channel morphology change after restoration: Drone laser scanning versus traditional surveying techniques, Hydrology.

  5. Christensen, N.D., E.M. Prior, J.A. Czuba, and W.C. Hession (2024), Stream restoration that allows for self-adjustment can increase channel-floodplain connectivity, Journal of Ecological Engineering Design, 1 (1). https://doi.org/10.21428/f69f093e.e8ffa1a3.

  6. Sumnall, M.J., T.J. Albaugh, D.R. Carter, R.L. Cook, W.C. Hession, O.C. Campoe, R.A. Rubilar, R.H. Wynne, and V.A. Thomas (2023), Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L.plantations, International Journal of Remote Sensing, 44:1, 217-247. doi/10.1080/01431161.2022.2161853

  7. Oakes J, Balota M, Cazenave A-B, Thomason W. Using Aerial Spectral Indices to Determine Fertility Rate and Timing in Winter Wheat. Agriculture. 2024; 14(1):95. https://doi.org/10.3390/agriculture14010095

  8. Johnson, Kellie; Drape, Tiffany; Oakes, Joseph; Simpson, Joseph; Brown, Ann; Westfall-Rudd, Donna M.; and Duncan, Susan (2023) "An Interdisciplinary Approach to Experiential Learning in Cyberbiosecurity and Agriculture Through Workforce Development," Journal of Cybersecurity Education, Research and Practice: Vol. 2024, Article 2. Available at: https://digitalcommons.kennesaw.edu/jcerp/vol2024/iss1/2

  9. Dhakal, K.; Sivaramakrishnan, U.; Zhang, X.; Belay, K.; Oakes, J.; Wei, X.; Li, S. Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels. Sensors2023, 23, 3523. https://doi.org/10.3390/s23073523

  10. Jordan, D. L., Anco, D., Balota, M., Langston, D., Lux, L., Shew, B., & Brandenburg, R. L. (2024). Survey of herbicide and fungicide use in peanut in North Carolina and Virginia in the United States. Crop, Forage & Turfgrass Management, 10(1), e20263.

  11. Jordan, D. L., Anco, D., Balota, M., & Brandenburg, R. L. (2024). Farmer insights on harvesting peanut: A survey from the Virginia–Carolina region of the United States. Crop, Forage & Turfgrass Management, 10(1), e20262.

  12. Jordan, D. L., Shew, B. B., Brandenburg, R. L., Anco, D., & Balota, M. 2023. Summary of tillage practices in peanut in the Virginia–Carolina region of the United States. Crop, Forage & Turfgrass Management, 9(1), e20222.

  13. Oteng-Frimpong R, Karikari B, Sie EK, Kassim YB, Puozaa DK, Rasheed MA, Fonceka D, Okello DK, Balota M, Burow M and Ozias-Akins P (2023) Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea) germplasm. Front. Plant Sci. 13:1076744.doi: 10.3389/fpls.2022.1076744

  14. Lachowiec J, Feldman MJ, Matias FI, LeBauer D, Gregory A. (2024) ”Adoption of unoccupied aerial systems in agricultural research”. The Plant Phenome Journal. 7(1): e20098

  15. Prince Czarnecki, Joby M., et al. "Estimation of the economic impacts and operational limitations imposed on unmanned aerial systems by poor sky conditions." Precision Agriculture6 (2023): 2607-2619.Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science? (Trends in Plant Science)

  16. Czarnecki, J. M. P., et al. "Evaluating the spectral response of cotton and corn to different cover crops using UAV imagery." Precision agriculture’23. Wageningen Academic Publishers, 2023. 677-683.High-accuracy infrared thermography of cotton canopy temperature by unmanned aerial systems (UAS): Evaluating in-season prediction of yield (Smart Agricultural Technology)

  17. Bagnall, G. Cody, et al. "Uncrewed aerial vehicle radiometric calibration: A comparison of autoexposure and fixed‐exposure images." The Plant Phenome Journal1 (2023): e20082.Plastic Contaminant Detection in Aerial Imagery of Cotton Fields Using Deep Learning (Agriculture)

  18. Yadav, Pappu Kumar, et al. "Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery." Computers and Electronics in Agriculture204 (2023): 107551.

  19. Nguyen, A., J.P. Ore, C. Castro-Bolinaga, S.G. Hall, S. Young, 2024. Towards Autonomous, Optimal Water Sampling with Aerial and Surface Vehicles for Rapid Water Quality Assessment American Society of Agricultural and Biological Engineers 67 (1), 91-98

  20. Howell AW, Leon RG, Everman WJ, Mitasova H, Nelson SAC, Richardson RJ. Performance of unoccupied aerial application systems for aquatic weed management: Two novel case studies.Weed Technology. 2023;37(3):277-286. doi:10.1017/wet.2023.32 (Selected as Outstanding Paper in Weed Technology for 2023)

  21. Rockstad, G. B. G., Austin, R. E., Gouveia, B. T., Carbajal, E. M., & Milla-Lewis, S. R. (2023, December 19). Assessing unmanned aerial vehicle-based imagery for breeding applications in St. Augustinegrass under drought and non-drought conditions. Crop Science, Vol. 12. https://doi.org/10.1002/csc2.21128

  22. Jones, Eric A. L., Robert Austin, Jeffrey C. Dunne, Charles W. Cahoon, Katherine M. Jennings, Ramon G. Leon, and Wesley J. Everman. Utilization of Image-Based Spectral Reflectance to Detect Herbicide Resistance in Glufosinate-Resistant and Glufosinate-Susceptible Plants: A Proof of Concept. Weed Science, December 19, 2022, 1–11. https://doi.org/10.1017/wsc.2022.68.

  23. Ranjan R., Amogi, B. R., Chandel, A. K., Khot, L. R., Sallato, B. V. and Peters, R. T. (2024). Efficacy evaluation of apple sunburn mitigation techniques in WA 38 cultivar using crop physiology sensing system. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108501

  24. Molaei, B., Chandel, A. K., Peters, R. T., Khot, L. R., Khan, A., Maureira, F. and Stockle, C. (2023). Investigating the application of artificial hot and cold reference surfaces for improved ETc estimation using the UAS-METRIC energy balance model. Agricultural Water Management, 284, 108346. https://doi.org/10.1016/j.agwat.2023.108346

  25. Molaei, B., Peters, R. T., Chandel, A. K., Khot, L. R., Stockle, C. O. and Campbell, C. S. (2023). Measuring evapotranspiration suppression from the wind drift and spray water losses for LESA and MESA sprinklers in a center pivot irrigation system. Water, 15(13), 2444. https://doi.org/10.3390/w15132444

  26. Betitame, Kelvin, et al. "Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies." (2024): 63-76.

  27. Delavarpour, Nadia, et al. "A review of the current unmanned aerial vehicle sprayer applications in precision agriculture." (2023): 703-721.

  28. G Chen, L Li, H Zhang, Z Shi, B Shang. (2023). Aerial Nondestructive Testing and Evaluation (aNDT&E). Materials Evaluation 81 (1): 67–73 https://doi.org/10.32548/2023.me-04300

  29. Zhang, Liangji & Lu, Chao & Xu, Haiwen & Chen, Aibin & Li, Liujun & Zhou, Guoxiong. (2023). MMFNet: Forest Fire Smoke Detection Using Multiscale Convergence Coordinated Pyramid Network with Mixed Attention and Fast-robust NMS. IEEE Internet of Things Journal. PP. 1-1. 10.1109/JIOT.2023.3277511.

  30. Zhang, Yukai & Zhou, Guoxiong & Chen, Aibin & He, Mingfang & Li, Johnny & Hu, Yahui. (2023). A precise apple leaf diseases detection using BCTNet under unconstrained environments. Computers and Electronics in Agriculture. 212. 108132. 10.1016/j.compag.2023.108132.

  31. Li, Mingxuan & Zhou, Guoxiong & Chen, Aibin & Li, Liujun & Hu, Yahui. (2023). Identification of tomato leaf diseases based on LMBRNet. Engineering Applications of Artificial Intelligence. 123. 106195. 10.1016/j.engappai.2023.106195.

  32. Zhan, Jialei & Xie, Yuhang & Guo, Jiajia & Hu, Yaowen & Zhou, Guoxiong & Cai, Weiwei & Wang, Yanfeng & Chen, Aibin & Xie, Liu & Li, Maopeng & Li, Liujun. (2023). DGPF-RENet: A Low Data Dependency Network With Low Training lterations For Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-1. 10.1109/TGRS.2023.3306891.

  33. Ladino, K.S., Sama, M.P. 2024. A Method for Evaluating Global Navigation Satellite System Position Accuracy in Small Unmanned Aircraft Systems. Journal of the ASABE. Vol 67(2): 153-167. https://doi.org/10.13031/ja.15890

  34. Agioutanti, R., Ford, W.I., Sama, M.P., McGill, T. 2024. Impacts of aquatic vegetation dynamics on nitrate removal in karst agricultural streams: Insights from UAS imagery and in situ sensing. Journal of the ASABE. Vol. 67(2): 89-104. https://doi.org/10.13031/ja.15791

  35. Bailey, S.C., Smith, S.W., Sama, M.P., Al-Ghussain L., de Boer, G. 2023. Shallow Katabatic Flow in a Complex Valley: An Observational Case Study Leveraging Uncrewed Aircraft Systems. Boundary-Layer Meteorology. 186(2): 399-422.
    https://doi.org/10.1007/s10546-022-00783-w

  36. Dhakal, R., Maimaitijiang, M., Chang, J., & Caffe, M. (2023). Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning. Sensors, 23(24), 9708.

  37. Kaushal, S., Gill, H.S., Billah, M.M., Khan, S.N., Halder, J., Bernardo, A., Amand, P.S., Bai, G., Glover, K., Maimaitijiang, M. and Sehgal, S.K., 2024. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning. Frontiers in Plant Science, 15, p.1410249.

  38. Khan, S.N., Maimaitijiang, M., Millett, B., Paheding, S., Li, D., Caffé, M. and Kovács, P., 2023, July. Simultaneously Estimating Crop Yield and Seed Composition using Multitask Learning from UAV Multispectral Data. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 2771-2774). IEEE.

  39. Maimaitijiang, M., Millett, B., Paheding, S., Khan, S.N., Dilmurat, K., Reyes, A.A. and Kovács, P., 2023, July. Estimating Crop Grain Yield and Seed Composition Using Deep Learning from UAV Multispectral Data. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 3546-3549). IEEE.


Extension Publications



  1. Chandel, A.K., Langston, D., 2023. Aerial multispectral imagery for high-throughput mapping of spatial corn yield potentials. VCE Publications, SPES-526NP.

  2. Chandel, A.K., Langston, D., 2023. Aerial multispectral imagery for high-throughput mapping of spatial soybean yield potentials. VCE Publications, SPES-527NP.

  3. Chandel A.K., 2023. Aerial imagery to improve disease diagnosis and management in field crops. VCE Publications, SPES-515NP.

  4. Lee, J. Oakes. Effective Tiller Management for Winter Wheat. 2023. https://www.pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/spes/spes-431/SPES-431.pdf

  5. Joseph Oakes. Aerial Spectral Imagery to Determine Wheat Fertility Rate and Timing. 2024. https://www.pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/spes/spes-582/SPES-582.pdf

  6. Young, P. Ore, S. Hall, 2023.  The Coming Wave of Aquatic Robotics, Resource Magazine, https://tinyurl.com/4crkbb8j

  7. Amogi, B. R., Schrader, M. J., Khot, L. R. and Hoheisel, G. A. (2023). Decision support tools for frost mitigation. Washington State University - Viticulture and Enology Extension News, Spring 2023, 6-7

  8. Jackson, J.J., Ladino, K.S. 2024. Drone Sprayer Sizing for Agricultural Applications AEN-174. University of Kentucky Cooperative Extension Service. https://www2.ca.uky.edu/agcomm/pubs/AEN/AEN174/AEN174.pdf

08/18/2025

11/12/2026

Journal articles, pre-prints published



  1. Farina, M., W. Christian, N. Hasson, T. McDermott, S. Powell, R. Hatzenpichler, H. Webb, G. LaRue, K. Okano, E. Sproles, and J. Watts. (2025). Methane flux spatial heterogeneity driven by surface and subsurface conditions in boreal forest-fen mosaic. Environmental Research Letters 20 104029, DOI: 10.1088/1748-9326/adff9a

  2. Ermatinger, L., S. Powell, R. Peterson, D. Weaver. 2025. Mapping wheat stem sawfly (Cephus cinctus Norton) infestations in spring and winter wheat fields via multiway modelling of multitemporal Sentinel 2 images. Remote Sensing 17(11), 1950; https://doi.org/10.3390/rs17111950​

  3. Miller, J. O., de Barros, P. R., Schulenburg, A. N., & Tully, K. L. (2025). Coastal stressors reduce crop yields and alter soil nutrient dynamics in low-elevation farmlands. Discover Agriculture, 3(1), 119.

  4. Jjagwe, P., Chandel, A.K. and Balota, M. (2026). High-Throughput Phenotyping of Faba Bean Crop Using Remote Sensing Technologies and Data Analytics: A Systematic Assessment of Status and Trends Over Past Decade. Agricultural Research, pp.1-15. https://doi.org/10.1007/s40003-026-01009-y

  5. Manimozhian, A., Jjagwe, P., & Chandel, A. K. (2026). A UAV-to-Satellite Scaling Framework for Monitoring Cotton Boll Opening Using Sentinel-2 Earth Observations. Land, 15(8), 1361. https://doi.org/10.3390/land15081361

  6. Manimozhian, A.P, Chandel, A.K.C. (2026). Thermal Infrared Technologies for Precision Agriculture: A ROSES-Guided Systematic Evidence Synthesis on Platforms, Calibration, and Digital Analytics. Agricultural Environment and Sustainability, p.100014. https://doi.org/10.1016/j.ages.2026.100014

  7. Jung, M., Chang, A.*, Cannon, C.H., Rivas-Torres, G., Jung, J. 2026. “Comprehensive uncrewed aerial system data for Amazon rainforest at Tiputini Biodiversity Station, Ecuador”, Scientific Data 13, Article Number: 532 (2026)

  8. Li S, Yang Y, Zhang J, Wilson LT, Samonte SO, Dou F, Bera T, Zhou X-G & Sanchez D (2025) UAV image analysis for detecting rice seedling gaps and gap effect on grain yield. Smart Agricultural Technology 10 (https://doi.org/10.1016/j.atech.2024.100753).

  9. Pham T-H, Yang Y, Zhang J, Li S, Samonte SOPB, Dou F, Sanchez D, Wilson LT, Bera T, Wang J & Nguyen K-D (2026) Automatic measurement of rice tiller angle from unmanned aerial vehicle images. The Plant Phenome Journal. https://doi.org/10.1002/ppj2.70089.

  10. Shrestha, S. , P.D. Alderman, G. Ojha† and B.F. Carver. 2026. Multiple ortho-mosaicking software pipelines produce comparable imagery-derived wheat phenotypes. The Plant Phenome Journal. 9(1):e70064

  11. Asfaw, B. W., Fuka, D. R., Collick, A. S., White, R. R., & Easton, Z. M. (2025). Characterizing the topographic index as a tool to represent spatial soil moisture: The effect of classification approach, digital elevation model type, and resolution. Vadose Zone Journal, 24(4). https://doi.org/10.1002/vzj2.70031

  12. Asfaw, B. W., Maksud, S., Fuka, D. R., Collick, A. S., White, R. R., & Easton, Z. M. (2025). Calibrating on Downscaled Satellite Soil Moisture Data Can Improve Watershed Model Performance in Predicting Soil Moisture Variability. https://doi.org/10.5194/egusphere-2025-5813

  13. Betitame, K., Igathinathane, C., Howatt, K., Mettler, J., Koparan, C., & Sun, X. (2025). A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance. Journal of Agriculture and Food Research, 20, 101784. doi:https://doi.org/10.1016/j.jafr.2025.101784

  14. Soto, M., Poncet, A. M., Roma-Burgos, N., France, O. W., Velasquez, J. C., Ashworth, A. J., Koparan, C. (2025). Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.). Smart Agricultural Technology, 11, 100890. doi:https://doi.org/10.1016/j.atech.2025.100890

  15. Tagoe, A.; Azmir, M. N.; Nores, M.; Elli, E. F.; Jhonson, D.; Wardlow, G.; Koparan, C. (2026), UAV-Assisted Corn Biomass Estimation from RGB Monocular Depth, Smart Agricultural Technology, Pending Review, Submitted on 7/24/2026, ATECH-D-26-02558

  16. Azmir, M. N.; Tagoe, A.; Runkle, R.; Wang, D.; Burgos, N. R.; Koparan, C. (2026), UAV Integrated Adaptive Sprayer Control System Development for Spot Spraying in Soybean, Smart Agricultural Technology, Pending Review, Submitted on 7/24/2026, ATECH-D- 26-02559.

  17. Tagoe, A.; Koparan, C.*; Worthington, M.; Johnson, D.; Wang, D.; Wardlow, G. W. (2026), Object Detection Algorithm Evaluation on Blackberry Flower Quantification from UAV Imagery, Journal of Agriculture and Food Research, Pending Review, Submitted on 8/11/2026 

  18. Kaushal, S., Billah, M.M., Singh, M., Koupal, D., Sitaula, P.R., Halder, J., Sidhu, J., Maimaitijiang, M. and Sehgal, SK., 2026. UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability. The Plant Phenome Journal, 9(1), p.e70085. 

  19. Billah, M.M., Maimaitijiang, M., Kaushal, S., Millett, B., Khan, S.N., Halder, J., Kleinjan, J. and Sehgal, S.K*., 2026. Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning. Frontiers in Plant Science, 17, p.1812052. 

  20. Janjua, U., Maimaitijiang, M., Billah, M.M., Millett, B., Dorissant, L., Acharya, I. and Kovács, P., 2026. Assimilation of UAS remote sensing and deep learning-derived crop parameters into DSSAT model for grain yield prediction. Smart Agricultural Technology, p.102101. 

  21. Maimaitijiang, M., Yabwalo, D., Billah, M.M., Millett, B., Sehgal, S.K. and Ali, S., 2025. Estimating wheat disease severity from high-resolution UAV multispectral imagery using deep learning. Smart Agricultural Technology, p.101729. 

  22. Maimaitijiang, M., Ghimire, H., Thapa, S., Billah, M.M., Sehgal, S., Singh, M., Kaushal, S., Poudel, K., Subedi, S., Janjua, U.U.R. and Makonga, L.O., 2026. WheatAI v1. 0: An AI-Powered High Throughput Wheat Phenotyping Platform. arXiv preprint arXiv:2601.08863. 

  23. Swaminathan, V., Thomasson, J. A., Rajan, N., & Hardin, R. G. (2025). Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments. Remote Sensing, 17(4), 579.


 Scientific Presentations given



  1. Farina, M., J. Watts, and S. Powell. 2026. Spatial and temporal trends in Arctic-Boreal carbon fluxes. Poster Presentation. Crawford Scholars Project Showcase, Bozeman, MT, April 16, 2026

  2. Rashed, A., M. Farina, K. Intemann, and S. Powell. 2025. Comparing PM2.5 concentration changes before, during, and after prescribed fires. Poster Presentation. SMART FIRES annual meeting, September 8, 2025.

  3. Miller, J.O. Using Apparent Electrical Conductivity to Map Coastal Salinity. 2025. Northeast Soil Testing Workgroup Annual Meeting. Milford, PA. October 2025.

  4. Miller, J.O. Using Apparent Electrical Conductivity to Map Coastal Soils. Mid Atlantic Crop Management School. Ocean City, MD. November 2025.

  5. Miller, J.O. 2026. Interpreting Soil Tests and Drone Maps for Coastal Soil Salinity. Delaware Ag Week. Harrington, DE. January 2026.

  6. Miller, J.O., 2026. Sources and Impacts of Salinity in Coastal Soils. New Jersey Ag Expo. Atlantic City, NJ. January 2026.

  7. Miller, J.O. 2026. Using Apparent Electrical Conductivity to Map Coastal Salinity. YES Conference. Wilmington, DE. February 2026

  8. None

  9. Fulton, J. P., Thomas, A., Shearer, S. A., Hawkins, E., & Khanal, S. (2026). Performance of unmanned aerial vehicles to broadcast-interseed cover crops at different crop stages. In Proceedings of the 17th International Conference on Precision Agriculture and 11th Brazilian Congress on Precision and Digital Agriculture, Porta Alegra, Brazil.

  10. Jjagwe, P., Chandel, A. K., Balota, M., Seetharaman, R., and Nkwocha, C. L., Flessner, M. L. 2026. Weed detection and mapping in faba bean using UAS imagery and deep learning techniques. ASABE AIM conference, Indianapolis, IN, July 12-15, 2026.

  11. Jjagwe, P., Chandel, A. K., Balota, M., Seetharaman, R., and Nkwocha, C. L. 2026. Faba bean crop detection and mapping using UAS imagery and deep learning methods. ASABE AIM conference, Indianapolis, IN, July 12-15, 2026.

  12. Jjagwe, P.G, Chandel A.K., Balota, M., Seetharaman, R.G, Nkwocha, C.G, Bandi, S., Konduru, L.V., Yadav, V., 2025. Weed detection in faba bean using unoccupied aerial system imagery and computer vision models. SPIE Defense + Commercial Sensing exhibition, April 26-30, 2026, National Harbor, MD. (oral presentation).

  13. Jjagwe, P.G, Chandel, A.K., Balota, M., Seetharaman, R.G, Raman, R., 2026. Faba bean crop identification and mapping from UAS RGB imagery using deep learning approaches. AI in Agriculture Conference, March 31- April 2, 2026, Raleigh, NC. (poster presentation).

  14. Raymond, S.G, Chandel, A.K., Balota, M., 2026. Web-Based Platform for Assessing Peanut Pod Maturity Using Aerial Spectral Imagery and Multiview Machine Learning. AI in Agriculture Conference, March 31- April 2, 2026, Raleigh, NC. (poster presentation).

  15. Sigdel, S., Chang, A., 2026. “Imported Fire Ants (IFA) Mounds Detection Using RGB and Thermal UAS Imagery based YOLO Deep Learning Model”, 2026 AI in Agriculture Conference, March 31 - Apr. 2, 2026, Raleigh, NC

  16. Erdemir, E., Diktaş, E.D., Bilgin, B., Sekmen, A., Chang, A., 2026. “Per-Pixel Crop Yield Estimation via Spatially Aligned Harvester Telemetry and Multi-Band Temporal Sentinel-2 Data with Explainability Analysis”, International Selcuk Agricultural Sciences Congress, May 6 - 8, 2026, Selcuk University, Turkey

  17. Chang, A., Sigdel, S., 2026. “Performance of Multi-time and -sensor UAS data for Imported Fire Ants (IFA) Mound Detection”, 2026 ASABE AIM, July 12 - 15, 2026, Indianapolis, IN, USA

  18. Chang, A., Song, A., 2026. “A Post-Hurricane Building Roof Damage Assessment Using Multi-Source Aerial Observations”, UKC 2026, August 4 - 8, 2026, Orlando, FL, USA

  19. Poudel, A., P.D. Alderman, M. Aoun, B.F. Carver and G. Ojha. 2025. UAS-Based

  20. Feature Engineering for Barley Yellow Dwarf Disease Prediction in Winter Wheat.

  21. Presented at CANVAS 2025, 9–12 November 2025, Salt Lake City, Utah, USA.

  22. Shreshtha, S., P.D. Alderman and B.F. Carver. 2025. Developing Non-Linear

  23. Models for Deriving Dynamic Winter Wheat Phenotypes from UAS-Based Imagery.

  24. Presented at CANVAS 2025, 9–12 November 2025, Salt Lake City, Utah, USA.

  25. Alderman, P.D., S. Shreshtha and B.F. Carver. 2025. Inferring Process-Based-Model-

  26. Derived Wheat Phenotypes Using Field-Based and UAS Imagery Data. Presented at

  27. CANVAS 2025, 9–12 November 2025, Salt Lake City, Utah, USA.

  28. Sigdel, S., Chang, A., 2026. “Imported Fire Ants (IFA) Mounds Detection Using RGB and Thermal UAS Imagery based YOLO Deep Learning Model”, 2026 AI in Agriculture Conference, March 31 - Apr. 2, 2026, Raleigh, NC

  29. Chang, A., Sigdel, S., 2026. “Performance of Multi-time and -sensor UAS data for Imported Fire Ants (IFA) Mound Detection”, 2026 ASABE AIM, July 12 - 15, 2026, Indianapolis, IN, USA

  30. Davis, R., Majumdar, N., & Koparan, C. Simulation-Based Analysis of Autonomous UAV Swarm Spraying for Precision Agriculture. In AIAA AVIATION 2026 Forum.

  31. Azmir, M. N., Koparan, C., Roma, N. R., Runkle, B., & Wang, D. (2026). Computer Vision-Based Early Red Rice Detection System Development for Mechanized Row Rice Farming. Paper presented at the 2026 ASABE Annual International Meeting, St. Joseph, MI. https://elibrary.asabe.org/abstract.asp?aid=56017&t=5

  32. Tagoe, A., Koparan, C., Worthington, M., Johnson, D., & Wang, D. (2026). UAV-Based Yield Prediction and Phenology-Structured Feature Learning in Blackberry. Paper presented at the 2026 ASABE Annual International Meeting, St. Joseph, MI. https://elibrary.asabe.org/abstract.asp?aid=56033&t=5

  33. Tagoe, A.; Azmir, M. N.; Munagala, S.; Wang, D.; Koparan, C. (2026), Robot Prototype for Corn Stand Population Mapping and Management, Conference Proceeding, Standard Division Report, ASABE AIM, Indianapolis, IN.

  34. Reddy, C.; Feng, Y.; Tripathy. A.; Mahmoudi, S.; Wang, D.; Koparan, C. (2026), Robot Prototype for Corn Stand Population Mapping and Management, Conference Proceeding, Advanced Division Report, ASABE AIM, Indianapolis, IN.

  35. Maimaitijiang, M. et al. (2026). High-Throughput Plant Trait Quantification Using High-Resolution Imaging and Deep Learning. 2026 Oat Global Field Day, June 29, 2026, Brookings, SD, USA.

  36. Maimaitijiang, M. et al. (2025). From Image to Insight: AI-Driven Wheat Monitoring and Yield Prediction with Multi-Scale Sensing. CANVAS 2025, Nov. 9-12, Salt Lake City, UT, USA.

  37. Poudel, K., Maimaitijiang, M., Subedi, S., Ghimire, H., Billah, M. M., Sehgal, S. (2026). Temporal Gap-Adaptive Deep Learning for Wheat Grain Yield and Quality Prediction Using Multitemporal UAS Multispectral Imagery. 57th South Dakota State Geography Convention, March 26–27, 2026, Brookings, SD, USA.

  38. Janjua, U.R., Maimaitijiang, M., Zhang, H., & Osterloh, K. (2026). Leveraging AlphaEarth Foundations Embeddings and Deep Learning for Fine-Scale Maize Grain Yield Estimation. 57th South Dakota State Geography Convention, March 26–27, 2026, Brookings, SD, USA.

  39. Kaushal, S., Sehgal, SK., Maimaitijiang, M., Billah, MM., Singh, M., Koupal, D., Gill, H., Thapa, S., Halder, J., Ghimire, H., Subedi S. (2025). Integrating Genomics and High-Throughput Phenotyping with Machine Learning for Predictive Breeding in Winter Wheat. CANVAS 2025, Nov. 9-12, Salt Lake City, UT, USA.

  40. Mahajan, K., Poudel, K., Oliveira, G., Maimaitijiang, M., Caffe, M. (2025). Deep Learning for High-Throughput Oat Biomass Prediction Using UAV-Based Multispectral Imagery. CANVAS 2025, Nov. 9-12, Salt Lake City, UT, USA.

  41. Islam, M. U., Chen, D., Thomasson, J. A., & Han, X. (2026, June). Autonomous navigation and dynamic obstacle avoidance for an agricultural mobile ground control point. In SPIE Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping XI, National Harbor, MD.

  42. Molaei, B., Lamichhane, B., Gupta, A., Kiraga, S., Chandel, A., Peters, T., Khot, L., Leib, B., & Stockle, C. O. (2025, November 11). Advancing site-specific irrigation with high-resolution evapotranspiration mapping in Washington and Tennessee: AI-driven approaches and ongoing efforts. CANVAS 2025. ASA, CSSA, & SSSA.

  43. Lamichhane, B., Molaei, B., Marek, G. W., Colaizzi, P. D., Evett, S. R., de Koff, J. P., Peters, T., Chandel, A., Khot, L., & Stockle, C. O. (2025, November 11). Predicting the radiometric temperature of a fully irrigated canopy based on meteorological data for improving UAS-based evapotranspiration estimation. CANVAS 2025. ASA, CSSA, & SSSA

  44. Khot, L. (2026). (Invited) Plenary talk on “AI-enabled, weather-guided precision stress management in horticultural crops”, Technical Session-Precision Horticulture and New Age Farming Technologies, 11th Indian Horticulture Congress and International Meet, Bangalore, India, November 6-9, 2025. Participants: ~60.

  45. Khot, L. (2026). (Invited) Keynote talk on “Smart Farms, Ag Cyber Physical Systems, and Metrology”, Technical Session-Precision Horticulture and New Age Farming Technologies, 2025 IEEE International Workshop on Metrology for Agriculture and Forestry, Bologna, Italy, October 28-30, 2025. Participants: ~120.

  46. Khot, L. (2026). (Invited) Distinguished Speaker talk “AI enabled smart farms: concept to reality in WA state”, Session on AI Technologies in Robotics Operations and Field Management, The 2nd Annual Expanding AI/AgAID Symposium, Orlando, Florida. September 25, 2025.

  47. Khot, L. (2026). (Invited) Keynote “Role of connectivity in enabling smart farms across WA state”, Regional Fiber Connect Workshop, Spokane, WA. September 16, 2025. Time: 25 min. Participants: ~70.

  48. Dadallage, K. M., Bui, K., Amogi, B. R., Khot*, L. R. (2026) A UAS-Based Real-Time Spatiotemporal Atmospheric Profiling for Effective Frost Mitigation in Fruit Crops) to 2026 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) (Submitted)


Dataset/software published



  1. Farina, M.K., W. Christian, J.D. Watts, T. Mcdermott, R. Hatzenpichler, G. Larue, S. Powell, H. Webb, N. Barnes, and K. Okano. 2024. Methane Flux and Environmental Data, North of Big Trail Lake, Goldstream Valley, AK. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2393

  2. Hession, W. C., L. Lehmann, and J. P. Resop. 2026. Virginia Tech StREAM Lab Spring 2017 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G95X275P.

  3. Hession, W. C., L. Lehmann, and J. P. Resop. 2026. Virginia Tech StREAM Lab Fall 2018 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9SQ8XN1.

  4. Hession, W. C., L. Lehmann, Y. Kobayashi, and J. P. Resop. 2026. Virginia Tech StREAM Lab Summer 2024 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9J67F57.

  5. Hession, W. C., L. Lehmann, A. Chandel, and J. P. Resop. 2026. Virginia Tech StREAM Lab Spring 2026 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9NZ85W7.

  6. Alderman, Phillip D. and Pratishtha Poudel. 2026. csmbuilder: A Collection of Tools for Building Cropping System Models. R package version 0.1.0. URL https://cran.r-project.org/package=csmbuilder. DOI 10.32614/CRAN.package.csmbuilder

  7. Hession, W. C., L. Lehmann, and J. P. Resop. 2026. Virginia Tech StREAM Lab Spring 2017 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G95X275P.

  8. Hession, W. C., L. Lehmann, and J. P. Resop. 2026. Virginia Tech StREAM Lab Fall 2018 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9SQ8XN1.

  9. Hession, W. C., L. Lehmann, Y. Kobayashi, and J. P. Resop. 2026. Virginia Tech StREAM Lab Summer 2024 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9J67F57.

  10. Hession, W. C., L. Lehmann, A. Chandel, and J. P. Resop. 2026. Virginia Tech StREAM Lab Spring 2026 Drone Lidar Survey. OpenTopography. https://doi.org/10.5069/G9NZ85W7.


Outreach events



  1. Maja, J. M., et al. UAV Workforce Development & Part 107 Training Workshop, Walterboro, South Carolina, April 2026. Program participants included farmers, veterans, students, and Extension professionals.

  2. Chandel, A.K., Jjagwe, P. Drone applications for precision crop management. Ag Extension agents field day. August 5, 2026. Suffolk, VA.

  3. Chandel, A.K. Precision Ag Technology Expo 2024. April 22, 2026. Tidewater AREC, Suffolk, VA.

  4. Chandel, A.K. Drones for agriculture. Kings Fork high school class lecture. January 21, 2026. Suffolk VA.

  5.  UAS Application Workshop for Highschool student at SD (June 2026).

  6. South Dakota State University 2026 Drone Day Event (April 2026

  7. Bhalekar, D. G., L. R. Khot and H. I. Contreras Muñoz. 2026. Drones in Agriculture. 22nd Annual Agriculture Safety Day, Wenatchee, WA, USA. February 25th, 2026. Time: 60 min. Participants ~25

  8. Bhalekar, D. G., L. R. Khot. 2026. UAVs For Pesticide Applications. 2026 Pacific Northwest Pesticide Inspector Training, Walla Walla, WA, USA. February 26th, 2026. Time: 50 min. Participants ~75

  9. Mupambi, G. 2026. Drones in Cranberry Ag Update. UMass Cranberry Station. 167 participants. 

  10. Wicks, R and Mupambi, G. FAA Remote Pilot Certification Training. UMass Cranberry Station, East Wareham. 1/22/26-1/23/26. 25 participants. 

  11. Wicks, R and Mupambi G. FAA Remote Pilot Certification Training. UMass Cranberry Station, East Wareham. 1/22/26-1/23/26. 24 participants 

  12. Mupambi G. 2026. Fertilizer Application Drone Workshop (Limited Space), UMass Cranberry Station, East Wareham. 15 participants.


 Extension Publications



  1. Miller, J.O. (2025). Where are Saline and Sodic Soils on Delmarva? University of Delaware Extension. https://www.udel.edu/academics/colleges/canr/cooperative-extension/fact-sheets/where-are-saline-and-sodic-soils-on-delmarva/.

  2. Manimozhian, A.; Jjagwe, P.; Chandel, A. Mapping Cotton Boll Opening at Field-Scale (%Open Bolls) Using UAV Imagery. Virginia Cooperative Extension. 22 February 2025. Available online: https://www.pubs.ext.vt.edu/BSE/bse-385.html

  3. WheatAI web application: wheatai.net (A high throughput wheat phenotyping platform)

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