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

(Multistate Research Project)

Status: Active

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SAES-422 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

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