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

Annual/Termination Reports:

[07/27/2022] [06/21/2023] [08/29/2024]

Date of Annual Report: 07/27/2022

Report Information

Annual Meeting Dates: 05/26/2022 - 05/27/2022
Period the Report Covers: 01/01/2021 - 05/26/2022

Participants

Brief Summary of Minutes

Minutes Attachment:

2021-2022 Annual Report7.27.2022.docx

Accomplishments

Publications

Impact Statements

Date of Annual Report: 06/21/2023

Report Information

Annual Meeting Dates: 06/01/2023 - 06/02/2023
Period the Report Covers: 01/01/1970 - 01/01/1970

Participants

Name Institution
In person:
Maria Balota Virginia Tech
Matthew Chappell Virginia Tech
Abhilash Chandel Virginia Tech
Keely Beard Virginia Tech
Kang Xia Virginia Tech
Nathan Sedghi Virginia Tech
Christy Chavis Virginia Tech
Vijay Singh Virginia Tech
Aditya Singh University of Florida
Sayantan Sarkar Texan A&M University
Ayush Kachrulal University of Florida
Harrison Smith University of Arkansas
Cengiz Koparan North Dakota State University
Joe Mari Maja Clemson University
Emma Schultz Mississippi State University
Varun Aggarwal Purdue University
Matias Dominguez Utah State University
Ravi Mural University of Nebraska
Alex Thomasson Mississippi State University
Gray Turnage Mississippi State University
Phil Alderman Oklahoma State University
Mahendra Bhandari Texas A&M University
Dharmendra Saraswat Purdue University
Jennifer Lachowiec Montana State University
Glen C. Rains University of Georgia
Seth Murray Texas A&M University
Joshua Jackson University of Kentucky

Virtually:
Lee Tarpley Texas A&M University
Maitiniyazi Maimaitijiang South Dakota State University
Aditya Singh University of Florida
Max Feldman USDA-ARS
Randy Raper Oklahoma State University
Cully Hession Virginia Tech
Robin White Virginia Tech
Steven Thomson USDA-NIFA
Suman L.

AG2PI Sponsored students attending the S1069 multi-state hatch project meeting in 2023
Students’ name, title, and affiliation:
1. Sayantan Sarkar (sayantan@tamu.edu), Postdoc. Res. Fellow, Texas A&M Univ.
2. Emma Schultz (emmaschultz25@gmail.com), Ph.D. Res. Ass., Mississippi State Univ.
3. Ayush Kachrulal Sharma (ayush-fs@pau.edu), Ph.D. Res. Ass., Univ. of Florida.
4. Varun Aggarwal (aggarw82@purdue.edu), Ph.D. Res. Ass., Purdue Univ.
5. Harrison Smith (hws001@uark.edu), Senior Grad. Ass., Univ. of Arkansas.
6. Matias Dominguez (a02423442@usu.edu), Fulbright Visiting Fellow, Utah State Univ.
7. Cengiz Koparan (cengiz.koparan@ndsu.edu), Assistant Prof., North Dakota State Univ.
8. Ravi Mural (ravimural@gmail.com), Res. Ass. Prof., Univ. of Nebraska.
9. Jeonghwa Kim (jeonghwa.kim111@gmail.com), Postdoc. Res. Fellow, North Dakota
State Univ.

Brief Summary of Minutes

Brief summary of minutes of annual meeting:

Drs. Maria Balota and Matthew Chappell (Virginia Tech) began the meeting with brief introductions to the meeting agenda and remarks about the facilities at Tidewater Agricultural Research and Extension Center (TAREC). This was followed by remarks from Dr. Randy Raper (Oklahoma State University, virtual), who acknowledged those who made significant contributions to securing an award for this proposal, Dr. Kang Xia (Virginia Tech), who presented an introduction to Virginia Tech’s Center for Advanced Innovation in Agriculture (CAIA) and their goals as a center, and Dr. Steven Thomson (USDA-NIFA), who gave general updates from USDA-NIFA. Afterwards, the group heard from Dr. Seth Murray (Texas A&M University), who presented remarks from Crop Science Society of America (CSSA) and AG2PI program updates.

Just before lunch, each participating university was given the opportunity to share accomplishments and updates from their respective institutions relevant to the project.

University Updates

Texas A&M University – Dr. Mahendra Bhandari and Dr. Lee Tarpley

North Carolina State University – Dr. Gary Roberson

Mississippi State – Dr. Alex Thomasson, Dr. Gray Turnage, and Emma Schultz (sponsored PhD student)

Montana State University – Dr. Jennifer Lachowiec

Oklahoma State University – Dr. Phil Alderman

University of Florida – Dr. Aditya Singh (virtually) and Ayush Kachrulal (sponsored PhD student)

Clemson University – Dr. Joe Mari Maja

Purdue University – Dr. Dharmendra Saraswat

University of Kentucky – Dr. Josh Jackson

University of Georgia – Dr. Glen C. Raines

Washington State University – Dr. Lav R. Khot

South Dakota State University – Dr. Maitiniyazi Maimaitijiang (virtually)

This was followed by guest speakers.

Speakers

Speaker 1: Vijay Singh

Weed management focus including herbicide application with UAV. Considering variables like UAV elevation, herbicide rate, application type (spot v. blanket), previous v. live identification of weeds in the field.

Speaker 2: Cully Hession

LiDAR for assessing floods and floodplains. One goal is estimating a the poorly characterized parameter roughness in hydraulic modeling by using LiDAR. Can examine flood extents and velocity to estimate roughness. Also using UAS for phenotyping the functionally extinct American chestnut. Tree branching shares similarities with river branching for purposes of phenotyping to inform breeding.

Speaker 3: Robin White

UAV for Animal Ag overview. Four main themes for use in animals identified 1) detection and tracking, 2) understanding how animals utilize pasture land, 3) health monitoring, and 4) grazing support. The use of UAS need to provide benefits over the current approaches in regard to improving profitability, improving producer quality of life, and improving animal well-being.

Speaker 4: Abhilash Chandel

sUAS for field cropping systems by developing metrics that are associated with difficult to assess traits. This includes effects of pests, diseases, and below-ground traits. Also considering the idea that crops can have multiple “issues” simultaneously—what are the metrics of those and can they be distinguished?

Afterwards, the group split for a few hours and the sponsored graduate students, post-docs, and professors were given a brief tour of TAREC fields, led by Dr. Chappell, where they met with a Virginia Tech graduate student Baker Alijawasim to hear about an ongoing study with anthracnose in strawberry. Meanwhile, voting members of project S1069 remained in the TAREC auditorium for the business meeting.

Business Meeting

Minutes from the 2022 meeting were reviewed and the style of the 2022 minutes was appreciated.

For the current report, due in 60 days, send the following to Maria Balota:

publications from May/June 2022-May 2023

presentations from today as ppt or pdf

Because we won the Multi-state research award, we have $15,000 to spend. Discussion on how to spend these funds included a) travel funding to support early career researchers for the 2024 meeting, b) hiring a grant writer for a large grant on behalf of the group, c) write a textbook (online and dynamic) and hire an illustrator, d) write a review article and fund a post-doc/student for this, and e) write a review and a focused article on GPS calibration. Another idea was that writing the review article first would lay the framework for a grant submission.

The discussion of the grant considered the NSF’s new directorate Technology, Innovation, and Partnerships, known to fund “user-inspired” applications. Discussion of paper writing would require a post-doc with limited funding who would work solely on the article for several months.

Conclusion was to think on the spending choices for a few weeks and see if someone wants to lead the paper. If the paper is not taken on with progress made by the end of 2023, funds will be used to support graduate student travel to the 2024 meeting.

Because the university updates were longer than requested on the agenda, a proposal to standardize them in the future was made. It was proposed that presentations should include no more than two slides, one with summary of activities and the second with the products from these activities. This strategy will help streamline the meeting next year, help with the activity reporting, and identification of S-1069 research impacts.

The 2024 meeting will be held in Bozeman, June 13-14. Large local optics and photonics industry and small grains production will likely be the highlights. The 2024 meeting could include updates from the FAA on UAS rules and interaction with growers using UAS. Potentially, Dr. Bajwa (Montana State) can present the group information on funding from community groups related to precision ag., and bring community groups that have been funded and received previews of tech from industry. A limited number of people could be invited for more in-depth talks on Montana-specific commodities.

At the 2022 meeting, no vice-chair was chosen, but Jennifer Lachowiec was elected secretary. Discussion on the location of the 2025 meeting, highlighted Joshua Jackson hosting the meeting at U of Kentucky. Therefore, a motion was made for the location of the 2025 meeting (Alex Thomasson seconded by Gary Turnage) and unanimously accepted. Maria Balota is chair, Jennifer Lachowiec is vice-chair, and Joshua Jackson is secretary through Sep 2023. Jennifer Lachowiec (Montana State) will then become chair and Joshua Jackson (Univ. of Kentucky) will be vice-chair. Mahendra Bhandari (Texas A&M) was nominated as secretary starting September 2023, a motion was made by Alex Thomason and seconded by Dharmendra, and unanimously accepted by the group.

Minutes Attachment:

Picture from the S.pdf

Accomplishments

Accomplishments: <ul> <li>Training <ul> <li>Prepared a UAS manual (Mahendra Bhandari and Lee Tarpley)</li> <li>Conducted a UAS workshop to train Honduras Ministry of Agriculture personnel on UAS data collection, processing, and analysis (February 5-10, 2023)</li> <li>Conducted workshop to train graduate students from wheat breeding programs across the country on UAS data collection, processing and analysis (May 23-25, 2023)</li> </ul> </li> <li>Development of a web-based user interface for processing and management of the UAS data (Mahendra Bhandari and Seth Murray)</li> <li>Development of remote sensing methods for quantifying levels of important traits of the crop vegetation and indices to provide simultaneous, sensitive, and specific crop evaluations (all participating universities).</li> <li>Plant Phenomics achievements from Montana to Texas.</li> <li>Extension and outreach: Abhilash Chandel organized a workshop with growers in Virginia to discuss their views for the need of drone and remote sensing field activities.</li> <li>Drone spraying/spreading & economics: research in Virginia and Washington State had intensified and future collaborations among these universities were formulated.</li> <li>Awarded first prize for diversity-related research in understanding barriers to entry for UAS use at the ASA-CSSA-SSSA 2023 Conference.</li> </ul> Impacts: Working together, in small or large groups, the S-1069 Team made significant progress towards discovery if new aspects of UAS applications in breeding, and agriculture production and natural resources; development of training materials for UAS applications; building platforms for UAS-based data analysis, storage and sharing; and extension and outreach for expansion of UAS-driven agriculture production.

Publications

Publications: <ol> <li>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.</li> <li>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</li> <li>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).</li> <li>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</li> <li>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. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.agwat.2023.108346&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=av%2BEfDMRL%2BDCzIbGwNR5QgL5rz%2BSOlx8OtuVNDXPKLc%3D&reserved=0">https://doi.org/10.1016/j.agwat.2023.108346</a>.</li> <li>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, <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2F10.0.68.252%2FActaHortic.2022.1346.68&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=FHQ30hvdHgFIZfsdZdXTEpagkBcl%2BE5HcB2RjlYVjm0%3D&reserved=0">https://10.17660/ActaHortic.2022.1346.68</a></li> <li>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 <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.inpa.2021.02.003&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=SLQGbnjnNLVuMp6WP3DasNwx5vXJ1teNrSNFZmHRlzI%3D&reserved=0">https://doi.org/10.1016/j.inpa.2021.02.003</a></li> <li>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; <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Frs14184454&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=31ByIpKvajl20WXdqHX%2BiefednnJtjM%2F%2FwS6ubkSn5Q%3D&reserved=0">https://doi.org/10.3390/rs14184454</a></li> <li>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.</li> <li>Amogi, B., J. Schrader, Khot, and G.-A. Hoheisel. 2023. Decision support tools for frost mitigation. Washington State University – Fruit Matters, March 2023. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftreefruit.wsu.edu%2Farticle%2Ffrost-mitigation%2F&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=OEWOX2YNwxMRRuKgIBL3IUx%2F3gn5Y1uxmeMGl3Pq0ok%3D&reserved=0">https://treefruit.wsu.edu/article/frost-mitigation/</a></li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol> Technical Presentations/Abstracts <ol> <li>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)  <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fams.confex.com%2Fams%2F20MOUNTAIN%2Fmeetingapp.cgi%2FPaper%2F402770&data=05%7C01%7Cmbalota%40vt.edu%7Cf7738810094a4469813f08db6902eac5%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638219233507538848%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=MWKqpg7vrh7H7%2BDtGzLvW8Uee5IyxjjHCjE9TRiApZA%3D&reserved=0">https://ams.confex.com/ams/20MOUNTAIN/meetingapp.cgi/Paper/402770</a> </li> <li>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.</li> <li>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).</li> </ol> <ol> <li>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.</li> <li>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).</li> <li>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.</li> </ol> Extension Outreach talks <ol> <li>(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.</li> <li>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.</li> <li>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.</li> <li>(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).</li> <li>(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.</li> </ol> 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, <a href="https://doi.org/10.5061/dryad.bk3j9kdhp">https://doi.org/10.5061/dryad.bk3j9kdhp</a>

Impact Statements

Date of Annual Report: 08/29/2024

Report Information

Annual Meeting Dates: 06/13/2024 - 06/14/2024
Period the Report Covers: 05/27/2023 - 06/01/2024

Participants

Attendees Institution
In person
Lav Khot Washington State University
Maitiniya Maimaitijian South Dakota State University
Max Feldman USDA-ARS Prosser, WA
Keshawa Madhubhanu Dadallage Washington State University
Ubaid Ur Rehman Janjua South Dakota State University
Brynna Bruxellas North Carolina State Univerisity
Bholuram Gurjar Texas A&M
Rajkumar Dhakar Washington State University
Cengiz Koparan University of Arkansas
Muhammad Irshad South Dakota State University
Jennifer Lachowiec Montana State University
Drue Erwin Montana State University
Connor Nelle Montana State University
Erik Killian Montana State University
Maria Balota Virginia Tech
Robert Austin North Carolina State Univerisity
Akwasi Tegoe University of Arkansas
Chamaporn Paiboonvorachat Washington State University
Dattatray Bhalekar Washington State University
Giverson Mupambi University of Massachusetts-Amherst
Scott Powell Montana State University
Johnny Li University of Idaho
Moses Chilenje University of Florida
Max Hooks
Mahendra Bhandari Texas A&M
Jose Landiver Texas A&M
Abhilash Chandel Virginia Tech
Alex Thomasson Mississippi State University
Virtual attendees
Lee Tarpley Texas A&M
Phil Alderman Oklahoma State University
Dharmendra Saraswat Purdue
Randy Price LSU AgCenter
Michael Sama University of Kentucky

Brief Summary of Minutes

Brief summary of minutes of annual meeting

Opening

Dr. Jennifer Lachowiec began the meeting with a land acknowledgement, welcome, introduction and agenda. Montana State University Vice President, Dean, and Director of the College of Agriculture and Montana Agriculture Experimental Station reported on the state of MSU’s enrollement and the new processing and precision agriculture efforts at MSU including reviving the ag engineering program.

Two guest speakers from Montana State University presented.

Lochlinn Ermatinger

Wheat stem saw fly has estimated damage of 80 million dollars per year in Montana (350 in total range). WSS infestation leads to lodging. Utilzing drone and satellite imagery to estimate location of wheat stem sawfly. Advocated for the use of N-way partial least squares regressio since it appropriately models repeated measures and response as a proportion.

Dr. Jared Beaver: MSU Wildlife Extension: Tech and Life Applications

Applied research to solve issues human-wildlife-livestock issues. Rural working lands: Water/food sustainability, climate resiliency. Wildlife values are changing.

Wildlife-Livestock: Depredation, disease, resources. Realtime detection and monitoring in Paradise valley of Montana and contrasting game cameras for reliability and accuracty.

Drones can fairly accurately capture animal density. Size variation, shape of thermal signature, group size can identify animals. Feral pigs vs. deer separable via AI classification.

University Updates

Montana State University: Jennifer Lachowiec

Oklahoma State University: Phil Alderman (virtual)

University of Florida: Moses Chilenje

USDA-ARS, Prosser, WA: Max Feldman

Virginia Tech: Maria Balota

South Dakota State University: Maitiniyazi Maimaitijiang

North Carolina State University: Robert Austin

University of Kentucky: Michael Sama

Washington State University: Lav Khot

University of Idaho: Johnny Li

Texas A&M: Mahendra Bhandari

Mississippi State: Alex Thomasson

University of Arkansas: Cenzig Koparan

Lunch Break

Business Meeting

Ten early career researchers joined this year’s meeting because of S1069 was awarded $15,000 in funds as an award for achivements.

Past minutes: Minutes summarized. Motion passes to approve minutes.

Officers

Current officers through September 2024 and move up a position each year:
- Chair: Jennifer Lachowiec
- Vice-Chair: Joshua Jackson (to host/coordinate meeting 2025)
- Secretary: Mahendra Bhandari (to host/coordinate meeting 2026)

Nominations for secretary (host in 2027)

Giverson Mupambi was nominated, seconded, and accepted nomination.

Next chair was to be Josh Jackson, who is not in attendance.

Location of next S1069 meeting:

We were contacted by NCERA180 (Precision Ag Technologies for Food Agriculture Energy Production) about the possibility of joint meeting at South Dakota State University.

Alex Thomasson: Periodic meeting of multi-state groups brings people together. Regional Director of Southern Ag Research Stations endorses it. Unsure if multi-state groups often meet together.

Jennifer Lachowiec: It also bridges funding issues for some groups as they are part of both organizations.

Max Feldman moves to approve joint meeting, all in favor. Jennifer Lachowiec will pass the message along to NCERA.

S1069 Member Maitiniya Maimaitijian is at South Dakota State University and will be liaison for the meeting with colleagues there.

If the joint meeting does not come together, Michael Sama, possibly with Josh Jackson, will host at University of Kentucky.

Review of proposal milestones and progress towards them

2023 Milestones

A. Funded project for collaborative development of extension/outreach training materials from corporate, state, and/or federal resources.

None noted

B. Funded project for collaborative research from corporate, state, and federal sources.

Texas A&M has potential joint projects with OK State University.

Small seed project between Max Feldman (USDA) and Oregon State and Mississippi State.

C. Functional and accessible website.

NIMMS website is sufficient.

Thomasson: This point is more relevant to the hiring of Anne Porter. More websites is a hassle.

Lachowiec: Diversify NIMs website.

Balota: Website would not upload videos/photos, only text.

Conclusion: add more information to the website and inquire about linking to share out information

2024 Milestones:

At least one set of extension/outreach training material to meet the needs elucidated from the needs assessment conducted in earlier years.

Jennifer: There are many extension publications showcased in updates.

At least three submitted multi-university research proposals to address needs elucidated from the assessment conducted in earlier years.

Thomasson: Need to facilitate interstate grad student communication via coordinated meeting or other mechanism.

Organize with commodity meetings or other meetings or host an additional conference

Coconut grant from AG2PI funded S1069 grad students at the 2023 meeting at Virginia Tech.

Feldman: 30-50k per year for potato projects. Some goes to students for training.

Lav Khot: USDA commissions might not fund external meetings. NIFA is already connected. Can NIFA come up with the money to send the students? Write a conference proposal.

Li: Need to make a compelling case that students will benefit from meeting. Could get 50k for conference.

Thomasson willing to take lead on conference proposal. Lav will support.

Lachowiec: UAS in Ag CAP

Would NIFA fund a CAP project that isn’t part of a commodity?

Alter the way we host S1069

Maria: Have graduate students present as a mini-conference as part of the presentation. Move updates to the form of a report and replace time with graduate student presentations.

Feldman: 1^st day: presentations focused on graduate student presentations. 2^nd day business meeting and updates.

At least one customizable course curriculum (up to 3 credits) and pertinent education material/modules development to meet the needs elucidated from the assessment conducted in earlier years.

Mahendra: Put together a small handbook on UAV operations and data collection. Allows you start a phenotyping program from scratch. Moving toward making non-DJI handbooks for different platforms to give people options. Possible source of money to train IPM extension agents.

UAV manuals for feature collection and data processing

On researchgate

There is a need for refined data collection protocols across multiple states. Data analysis can also be standardized. Dataset needs to be understandable across disciplines.

Feldman: Submitted proposal to regional commodity board for multiple positions with DJI drone and pay for student training and data analysis jam. Commodity Board did not fund. WSDA Crop is another option.

OR, WA, ID work closely together. TA&M could also join together in data analysis.

Lav: Can we make consistent curriculum for 1 day training? Need to communicate clearly to extension agents and students.

Jennifer: Imagery processing through a CAP?

Noted that we should reword future proposals to include “submission” of proposals, rather than “funding” of proposals

Presentations & Demonstrations from Local Companies

Bridger Photonics: Nathan Greenfield

Originally funded by grants and gov contracts for long- and short-range laser systems. 2019: Gas mapping lidar. Looking at efficiency of oil and gas supply chain. 2022: GML V2. Leak detection and repair. Emission analytics.

Resonon: Alexandre Lussier

Making hyperspectral imagery products commercially available.

Pika-L can be used indoors w/out radiometric calibration and outdoors with. Gimbal can be used if very high winds, but generally not needed.

Vision Aerial: Phil and Mots

US-made unmanned aerials systems. Onboard dampening systems for different frequency vibrations. All onboard controls. Full system will run ~$45k. Outdoor demonstration.

Day 2 Field Trips

Visit to Montana State University Barley Malt and Brewing Lab

Visit to Montana State University Red Bluff Research Ranch

Accomplishments

Working together, in small or large groups, the S-1069 Team made significant progress towards discovery if new aspects of UAS applications in breeding, and agriculture production and natural resources; development of training materials for UAS applications; building platforms for UAS-based data analysis, storage and sharing; and extension and outreach for expansion of UAS-driven agriculture production. The team is leading research using hyperspectral imagery, and machine learning in agricultural research and practice. A common theme across the discussions was the focus on precision agriculture, where tools such as remote sensing, deep learning, and AI are being leveraged to improve crop monitoring, yield prediction, and stress detection. These innovations are being applied to a wide range of crops, including wheat, corn, potatoes, and rice, and are also extending into areas like soil nutrient management, ecological sustainability, and water quality improvement. Several projects emphasized the importance of developing standardized protocols for UAS and the need for robust data management frameworks to handle the growing influx of information from various sensors and imaging systems. Moreover, there is a notable effort to bridge the gap between research and practical application, with many projects involving outreach initiatives to educate the next generation of scientists and practitioners. This collective push is leading to the creation of valuable datasets, software tools, and patents, which are expected to have lasting impacts on both academic research and real-world agricultural practices, driving towards more sustainable and efficient food production systems. Presentations given <ol> <li>Lachowiec J, Feldman MJ, Matias FI, LeBauer D. Empowering high-throughput phenotyping using drones/UAS. AG2PI community event UAV/UAS/Drone restrictions for agricultural research and how different organizations are dealing with them. January 8, 2024.</li> <li>Bruxellas, B., Austin, Milla-Lewis, S., UAVs, Machine Learning, and Deep Learning for Phenotypic Evaluation of Turfgrass, 2024 AI in Agriculture and Natural Resources Conference - College Station, TX - Apr. 15-17</li> <li>Bruxellas, B., Austin, R., Woodley, A., & Heitman, J. L. (2022) UAV-Based Characterization of Micro-Topographic Features for Use in Estimating Soil Moisture and Nitrous Oxide Emissions in Agriculture Fields. ASA, CSSA, SSSA International Annual Meeting, Baltimore, MD. https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/143615</li> <li>Howell A.W., Durham, M.W., Sperry, B.P., and Richardson, R.J., Aquatic Plant Management Society: Identifying Optimal Spray Components for Waterhyacinth Control with Unoccupied Aerial Sprayers</li> <li>Hall, S. and D. Smith, 2024 (upcoming). Novel aquacultural applications for individual and fleets of autonomous surface vehicles. Accepted for presentation at AQUA 2024, Copenhagen Denmark (Organized by European Aquaculture Society and World Aquaculture Society, August 26-30, 2024</li> <li>Hall, S., March 2024. Research and Extension at the Marine Aquaculture Research Center, presented at North Carolina Aquaculture Development Conference New Bern, NC. Included discussion of automated and autonomous systems being used and researched with focus on aquaculture and coastal management.</li> <li>Chowdhury, S., 2024 (Hall on PhD committee). Assessing the impact of oyster reefs on local shallow water dynamics in a tidally controlled environment, WRRI, March 2024.  This analytical approach was matched with data from both autonomous and automated surface vehicles and aerial data collection with a focus on water quality for oysters.</li> <li>Schrader, M. J., Amogi, B. R., and Khot, L. R. 2023. Dronesonde based temperature inversion strength monitoring and grower decision support tool for frost mitigation in perennial specialty crops. Paper no. 2301273, ASABE 2023 Annual International Meeting, Omaha, NE, July 9-12, 2023 (Oral Presentation).</li> <li>Schrader, M. J., Bhalekar, D. G., Sahni, R., and Khot, L. R. 2023. Efficacy of drone with angled spray boom for applications in modern VSP trained vineyards. Paper no. 2301268, ASABE 2023 Annual International Meeting, Omaha, NE, July 9-12, 2023 (Oral Presentation).</li> <li>Schrader, M. J., Molaei, B., Khot, L. R., Zhang, Q., Peters, R. T., Keller, M., and Stockle, C. 2023. Smart Farm concepts into action: Technological challenges faced to implement Smart Vineyard in WA State. Paper no. 2301274, ASABE 2023 Annual International Meeting, Omaha, NE, July 9-12, 2023 (Oral Presentation).</li> <li>Yumnam, K., Gorthi, S., Amogi, B. R., Hoheisel, G.-A. and Khot, L. R. 2023. Drone-sonde and ground sensing-based evaluation of wind machines effectiveness in Sweet Cherry spring frost mitigation. Paper no. 2301395, ASABE 2023 Annual International Meeting, Omaha, NE, July 9-12, 2023 (Oral Presentation).</li> <li>Yumnam, K., Kothawade, G. S., Blanco, V., Chandel, A. K., Kalcsits, L. and Khot, L. R. 2023. Developing understanding on suitability of aerial multispectral and thermal imagery in mapping crop water status of apple trees. Paper no. 2301342, ASABE 2023 Annual International Meeting, Omaha, NE, July 9-12, 2023 (Oral Presentation).</li> <li>Li, L. Invited guest lecture, University of Idaho, November 7, 2023, ‘Precision Agriculture Research in Idaho: Assessing Crop Health and Evapotranspiration with Time-series High-resolution UAV Remote Sensing Imagery. Idaho Water Resource seminar.</li> <li>Li, L. Invited guest lecturer, University of Idaho, Feb 7, 2023, ‘Advance Robotics Sensing, Control and Computing For Sustainable Agriculture and Infrastructural and Manufacturing Management’, Mechanical Engineering Department Graduate Seminar Series.</li> <li>Li, L. Invited Talk “Advance Robotics Sensing, Control and Computing for Smart Agriculture" by 2023 School of Mechanical and Materials Engineering Seminar Series at Washington State University, September 21, 2023</li> <li>Ladino, K.S., Sama, M.P. 2024. Automating Ground Control Point Detection in UAS Imagery Using Matrix Barcodes. SPIE Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX. National Harbor, MD.</li> <li>Wesche, J.A., Pampolini, L.F., Sama, M.P. 2023. Validating Drone-Based 3D Models Using a LiDAR 3D Scanner. ASABE Annual International Meeting. Omaha, NE.</li> <li>Ladino, K.S., Sama, M.P. 2023. Evaluating Static and Dynamic Position Measurement Accuracy on an Unmanned Aircraft System (UAS). ASABE Annual International Meeting. Omaha, NE.</li> <li>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, July 16 – 21st, Pasadena, CA, USA.</li> <li>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, July 16 – 21, Pasadena, CA, USA.</li> <li>Maimaitijiang M., Early forecasting of crop production and quality using UAV remote sensing and machine learning. 2023 AAG GPRM conference, October 6 – 7th, Sioux Falls, SD, USA.</li> <li>Billah, MM., Maimaitijiang, M., Millett, B., Kaushal, S., Kovacs, P., Sehgal, SK. (2023) Crop Yield and Seed Composition Estimation Using UAV Remote Sensing and Deep Transfer Learning. 2023 AAG-GPRM conference, October 6-7th, Sioux Falls, SD, USA</li> <li>Janjua, U., Maimaitijiang, M., Millett, B., Khan. S., Billah. M., Kaushal. S., Shires. M., Yabwalo. D., Sehgal. S., Ali. S. (2023) Wheat Fusarium Head Blight (FHB) Disease Detection using UAV Multispectral Imagery and Machine Learning. 2023 AAG-GPRM conference, October 6-7th, Sioux Falls, SD, USA</li> <li>Maimaitijiang M., When AI meets UAV Remote Sensing: Crop Monitoring in Support of Precision Agriculture and Plant Phenotyping. 2023 South Dakota Geospatial Conference, October 18 – 19th, Chamberlain, SD, USA</li> <li>Billah, MM., Maimaitijiang, M., Millett, B., Kaushal, S., Kovacs, P., Sehgal, SK. (2023) Estimating Crop Yield and Protein Content using UAV Remote Sensing and Advanced Transfer Learning. 2023 South Dakota Geospatial Conference, October 18-19th, Chamberlain, South Dakota, USA</li> <li>Janjua, U., Maimaitijiang, M., Millett, B., Billah. M., Shires. M., Yabwalo. D., Sehgal. S., Ali. S. (2023) Wheat Fusarium Head Blight (FHB) Disease Detection using UAV Multispectral Imagery and Machine Learning. 2023 USGS EROS Poster Symposium, November 15th, Sioux Falls, South Dakota, USA</li> <li>Kaushal, S., Gill, H., Halder, J., Billah, MM., Shahid, N., Maimaitijiang, M., Bernardo, A., St Amand, P., Bai, G., Sehgal, SK. AI-driven Winter Wheat Phenomics Prediction utilizing UAV Multispectral Data, 2024 Annual WheatCAP Meeting, January 11th, San Diego, CA, USA</li> <li>Kaushal, S., Gill, H., Halder, J., Billah, MM., Shahid, N., Maimaitijiang, M., Bernardo, A., St Amand, P., Bai, G., Sehgal, SK.  Improving Grain Yield Prediction with Multi-Modal Deep Learning, integrating Genomics and Phenomics, 2024 NAPPN Annual Conference, February 13-15th, West Lafayette, IN, USA</li> <li>Janjua U., Maimaitijiang, M., Millett, B., Billah. M, Shires. M., Yabwalo. D., Sehgal. S., Ali. S. (2024). Wheat Fusarium Head Blight (FHB) Disease Severity Estimation using UAV Multispectral Imagery and Machine Learning. 2024 NAPPN Annual Conference, February 13-15th, West Lafayette, IN, USA</li> <li>Janjua U., Maimaitijiang, M., Millett, B., Kovacs, P. (2024). Assimilation of UAS remote sensing and machine learning-based biomass with a crop simulation model for maize yield prediction. 2024 AI in Agriculture and Natural Resources Conference, April 15-17th, College Station, TX, USA</li> <li>Irshad, MA., Maimaitijiang, M., Millett, B., Kovacs, P. (2024). Large Scale Crop Canopy Cover Estimation Using Satellite-UAS Synergy and Machine Learning. 2024 AI in Agriculture and Natural Resources Conference, April 15-17th, College Station, TX, USA</li> <li>Billah, MM., Maimaitijiang, M., Millett, B., Kaushal, S., Kovacs, P., Sehgal, SK. (2024). Multiyear crop yield and quality estimation using UAS remote sensing and deep transfer learning. 2024 AI in Agriculture and Natural Resources Conference, April 15-17th, College Station, TX, USA</li> <li>Irshad, MA., Maimaitijiang, M., Millett, B., Kovacs, P. (2024). Crop Canopy Cover Estimation Using Satellite-UAS Synergy and Deep Learning. 2024 AAG Annual Conference, April 16 – 18th, Honolulu, HI, USA.</li> <li>Billah, MM., Maimaitijiang, M., Millett, B., Kaushal, S., Kovacs, P., Sehgal, SK. (2024). Crop yield and seed composition estimation using UAV remote sensing and deep transfer learning. 2024 AAG Annual Conference, April 16 – 18th, Honolulu, HI, USA.</li> <li>Janjua U., Maimaitijiang, M., Millett, B., Kovacs, P. (2024). Crop yield estimation using crop growth model, machine learning and remote sensing data assimilation. 2024 AAG Annual Conference, April 16 – 18th, Honolulu, HI, USA.</li> </ol> Patents <ol> <li>NDVI and NDRE Models to Determine Tiller Density in Winter Wheat; Authors: Oakes J; Balota M; Cazenave A-B; Thomason W</li> <li>Disclosure-24-00054. Khot, L. R., Dadallage, K. and Amogi, B. Weathersonde with integrated mobile/web application for real time mapping of lower atmospheric attributes</li> <li>Li, L. Unmanned vehicle having flight configuration and surface transverse configuration. US Provisional Patent 63/409,522 (Filed)</li> </ol> Dataset/software published <ol> <li>ArcGIS UAV Toolbox: GUI-based processing tools developed that facilitate UAV image processing and analysis in ArcGIS Pro, <a href="https://github.com/reaustin/arcgis-uav-toolbox">https://github.com/reaustin/arcgis-uav-toolbox</a></li> </ol> Outreach events <ol> <li>Chandel, A. Drone data analytics workshop, April 19, 2024</li> <li>Chandel, A. Virginia peanut production with state-of-art technology, March 8, 2024</li> <li>Chandel, A. Drone applications in strawberry production. Mid-Atlantic strawberry field day. February 26, 2024. Chesapeake, VA.</li> <li>Chandel, A. Drones and AI for peanut maturity mapping, Virginia peanut grower annual meeting, Franklin, VA. Feb 22, 2024.</li> <li>Chandel, A. Drone applications in agriculture. Private applicator recertification training. December 14, 2023. Rapidan, VA (Virtual)</li> <li>Lachowiec J. Research for early detection of herbicide resistant wild oat: send us your photos! Montana Grain Growers Association Annual Meeting, Great Falls, MT, November 29, 2023.</li> <li>Lav Khot. Co-organized “Smart Orchard + AgAID Field day” at Wenatchee, WA, September 15, 2023. This event featured the Smart Orchard project and the WSU AgAID AI Institute project developed technologies. Attendees: 40 tree fruit growers, agri-business industry, NIFA & NSF national program leaders, and media.</li> <li>Lav Khot. Co-organized “Smart Vineyard Field Day”, at Prosser, WA, July 21, 2023. This event showcased research to improve irrigation scheduling, grapevine plant models and heat stress mitigation techniques. Attendees: 40 grapevine growers, agri-business industry representatives, and media.</li> <li>Lav Khot. Co-organized “Smart Orchard Field Day”, Washington Fruit Smart Orchard Block, Grandview, WA. August 2, 2023. (Attendees: ~60+ from agri-business industry, growers, commodity group representatives, researchers, and media).</li> <li>Lav Khot. Co-organized “Spring Drone Day” at Pasco, WA; May 19, 2023. This event was co-organized with WSU Tree Fruit Extension team member, Bernardita Sallato and focused on educating stakeholders about current landscape of drone-based aerial imaging and crop management, offerings from service providers, and associated regulations. Attendees: 25+ Tree fruit growers, agri-business industry, and media.</li> <li>Lav Khot. 2023. (Invited) Virtual Talk on “Crop protection technologies for modern orchard systems”, 8th Annual Materials Innovation for Sustainable Agriculture Symposium, Orlando, FL, November 30, 2023. Time: ~30 min. Participants: ~50.</li> <li>Lav Khot. 2023. (Invited) Virtual Talk on “Aerial crop protection technologies for modern orchard systems”, 2023 Remotely Piloted Aerial Application Systems Workshop, University of California, Davis, CA; October 04, 2023. Participants: 104 (69 virtual, 35 in person).</li> <li>Lav Khot. 2023. (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.</li> <li>Lav Khot. 2023. 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.</li> <li>Lav Khot. 2023. 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.</li> <li>NC Aquaculture Industry Growth Visioning Workshop, March 21, 2024. Hall, moderator with Paige O’Neill, Focus: Recirculating Aquaculture Systems group.</li> <li>Hall, S., 2024. NC Aquaculture State Update and Economic impact NCSU-NCCE. Presented at NC Aquaculture Development Conference March 22, 2024.</li> <li>Hall, S., 2024. MARC Tour, with R. Kelly, M. Frinsko (35 participants), March 21, 2024.  Included discussion of automated and autonomous vehicles for aquacultural applications.</li> <li>Avi Goldsmith, Andrew Howell, Ramon Leon, NC Drone Extension Workshop (Talk and Field Demonstration), Intro to Unmanned Aerial Systems [Designed for NC Extension Personnel]</li> <li>Andrew W Howell and Robert J Richardson, Midwest Aquatic Plant Management Society Talk, Evaluation of Unoccupied Aerial Systems (UAS) for Aquatic Weed Management</li> <li>Andrew W Howell, North American Lake Management Society Talk, Unoccupied Aerial Systems (UAS) for Aquatic Weed Management</li> <li>Andrew W Howell, NC Crop Protection School Talk: Utilizing Unoccupied Aerial Spray Systems for Aquatic and Noncropland Weed Management</li> <li>Andrew W Howell, Michael W Durham, Benjamin P Sperry, and Robert J Richardson, South Carolina Aquatic Plant Management Society Talk, Identifying Optimal Spray Components for Floating Plant Control with Unoccupied Aerial Sprayers.</li> <li>Austin, R.E., State of Soybean Research in North Carolina. Evaluating drone and satellite imagery for on-farm decision support in North Carolina Soybeans, March 2024</li> <li>Austin, R.E., Ermish, S., Evaluating Image-based drone Stand Counts for Guiding Replant Decisions in Soybeans in North Carolina, 33nd Annual North Carolina Commodities Conference, January 11-13, 2023. Durham, NC. (~300 in attendance)</li> <li>Austin, R.E., Unmanned Aircraft in Sediment and Erosion Control Design, December 6th, 2023, Lake Wheeler Field lab, Raleigh, North Carolina, Raleigh, North Carolina (100 in attendance).</li> <li>Austin, R.E., Ermish, S UAV use for within season management decisions in North Carolina soybean production, Extension State Conference, Koury Convention Center, Greensboro, NC Oct 3-5th, 2023</li> <li>Austin, R.E., Ermish, S 32nd Annual North Carolina Commodities Conference, January 12-14, 2023. Durham, NC.</li> <li>ASABE Arkansas Section - Broadening Smart Technologies in Agricultural Systems Towards Autonomous Farming</li> <li>Arkansas Water Resources Conference - Unmanned Aerial Vehicles as Autonomous Water Quality Monitoring Platforms</li> <li>FAA high school visits over 80 students</li> <li>Li, L. Invited Talk “Drone remote sensing and AI for Precision Agriculture” by 2023 Hermiston Farm Fair on Nov 29-20, 2023, Oregon State University</li> <li>Maimaitijiang, M. UAS hands-on flight training and UAS remote sensing application workshop at Oglala Lakota Tribal college and Lower Brule High school (July 2023) <a href="https://lakotauas.wordpress.com/">https://lakotauas.wordpress.com/</a></li> <li>Maimaitijiang, M. 2024 South Dakota State University Drone Day (April 2024) <a href="https://www.sdstate.edu/news/2024/05/sky-limit-possible-uses-drone-technology">https://www.sdstate.edu/news/2024/05/sky-limit-possible-uses-drone-technology</a></li> </ol> Number of students supported/trained: 63 Grants funded <ol> <li>2023-2025. USDA-NIFA– ODF: Through University of Nebraska: National Agricultural Producers Data Cooperative: Strategic Development for USDA. ($920,000; PIs: Clarke, Chandel et al.).</li> <li>2022-2024. USDA-NIFA– ODF: Through University of Nebraska: National Agricultural Producers Data Cooperative: Building a Strategic Framework for Increasing Production and Driving Innovation. ($960,000; PIs: Clarke, Chandel et al.).</li> <li>2023-2024. NSF Convergence Accelerator– Precision agriculture for a resilient vegetable supply amidst climate change. ($313,032; PIs: Xia, Chandel, McNair, Mayer, Lyon-Hill, Herndon, Coggin, Higgins).</li> <li>2023-2023. Center for Advanced Innovation in Agriculture (VT-CAIA)– Supporting American Chestnut Hybridization for Restoration with State-of-the-Art Remote Sensing and Machine learning. ($10,000; PIs: Chandel, Hession).</li> <li>2022-2023. USDA-NIFA–ODF: Through University of Nebraska: Building synergistic data framework for cropping, livestock, and aquaculture producer network in Virginia. ($500,000; PIs: Clarke, Chandel et al.).</li> <li>2024-2025. Virginia Peanut Growers Association– Effect of Prohexadione calcium on peanut grown for seed production. ($28,500. PIs: Balota, Chandel, Langston).</li> <li>2023-2025. Southern Region Small Fruit Consortium– Latent detection of anthracnose on strawberry crop using multi-spectral imaging. ($10,000. PIs: Samtani, Chandel)</li> <li>2023-2024. Virginia Peanut Growers Association– Towards a grower-oriented decision tool for peanut maturity prediction and digging. ($17,760. PIs: Chandel and Balota).</li> <li>2023-2024. Impact of Planting Date on Wheat Development, Freeze Susceptibility, and Yield. $4,000. Joseph Oakes, Robbie Longest. Virginia Small Grains Board.</li> <li>Automated Satellite Detection & Drone Tasking Automation Service for Precision Agriculture. $10,000. Joseph Oakes. Pixelar, LLC., Virginia Innovation Partnership Corporation</li> <li>2023-2026. USDA-NIFA: Peanut Variety and Quality Evaluation - Harnessing Multiscale Data for Trait Prediction to Support Cultivar Release Decisions. Balota, Chandel et al. ($500,000).</li> <li>2023-2026. USDA-SCRI: Enhanced Mid-Atlantic System Sustainability Through Development Of High-Protein And Stress Tolerant Faba Bean For Winter Production. $2.7 million. Balota, Chandel, Oakes, et al.</li> <li>2023-2027: USAID-Peanut Innovation Lab: Adoption of High Throughput Phenotyping (HTP) in Varietal Development Throughout the Groundnut Improvement Network for Africa (GINA). Balota, Chandel et al. ($522,266)</li> <li>NSF/USDA, 1/1/21-12/31/24. NRI-INT: Developing a Customizable Fleet of Autonomous Co-Robots for Advancing Aquaculture Production. NSF/USDA $1.08M, Role: co-PI, $1.08M</li> <li>Evaluating UAV (drone) Use for within Season Management Decisions in NC Soybeans, NC Soybean Producers Association, Inc., $83,331</li> <li>Enabling the Research Farm of the Future, NC Plant Sciences Initiative, $315,077</li> <li>Unmanned Aerial Vehicle (UAV)-based turfgrass evaluation for selection and development of adapted cultivars, Center for Environmental Research and Education, $32,560</li> <li>Unmanned Aerial Vehicle for Pesticide Applications in the US, Mississippi State University - USDA NIFA, $69,955</li> <li>WaterSmartTurf: Bridging genome-to-phenome and release-to-adoption gaps for breeding and deployment of drought-tolerant warm-season turf grasses, USDA NIFA via University of Georgia, $1.9M</li> <li>Detection and management of multiple herbicide resistant weeds, USDA NIFA, Montana State University, $189,000.</li> </ol> Other <ol> <li>Khot, L. Teaching a graduate level course BYSE551: UAS in Agriculture (2 credits, Spring 2024)</li> <li>Li, L. Coordinating courses ME 201 Robotics Team Projects, ME401 Robotics Team projects</li> </ol>

Publications

Scientific/Research Publications <ol> <li>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. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Fland12122188&data=05%7C02%7Cmbalota%40vt.edu%7Cded81cb83b3846cba17b08dc501768d1%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638473308669061961%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=NXKKbG79PO2WbrHRbBoi0%2BZrB6v5zXoj9nlQ9%2FJsOe4%3D&reserved=0">https://doi.org/10.3390/land12122188</a></li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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). <a href="https://doi.org/10.21428/f69f093e.e8ffa1a3">https://doi.org/10.21428/f69f093e.e8ffa1a3</a>.</li> <li>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</li> <li>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. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Fagriculture14010095&data=05%7C02%7Cmbalota%40vt.edu%7Cbde88d8d1445400bf1cd08dc54a1e731%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638478301540328478%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=YKBMLWXOBai%2BLun3in5TERthaGyUTurWsHYS8UBg3CQ%3D&reserved=0">https://doi.org/10.3390/agriculture14010095</a></li> <li>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: <a href="https://digitalcommons.kennesaw.edu/jcerp/vol2024/iss1/2">https://digitalcommons.kennesaw.edu/jcerp/vol2024/iss1/2</a></li> <li>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. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Fs23073523&data=05%7C02%7Cmbalota%40vt.edu%7Cbde88d8d1445400bf1cd08dc54a1e731%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638478301540338219%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=bvPvB7%2FaGZEPnf3sJWtsc0tc4vBmJwqbXT6L3hK8enY%3D&reserved=0">https://doi.org/10.3390/s23073523</a></li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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</li> <li>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)</li> <li>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)</li> <li>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)</li> <li>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.</li> <li>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</li> <li>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)</li> <li>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</li> <li>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. <a href="https://doi.org/10.1017/wsc.2022.68">https://doi.org/10.1017/wsc.2022.68</a>.</li> <li>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. <a href="https://doi.org/10.1016/j.compag.2023.108501">https://doi.org/10.1016/j.compag.2023.108501</a></li> <li>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. <a href="https://doi.org/10.1016/j.agwat.2023.108346">https://doi.org/10.1016/j.agwat.2023.108346</a></li> <li>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. <a href="https://doi.org/10.3390/w15132444">https://doi.org/10.3390/w15132444</a></li> <li>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.</li> <li>Delavarpour, Nadia, et al. "A review of the current unmanned aerial vehicle sprayer applications in precision agriculture." (2023): 703-721.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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. <a href="https://doi.org/10.13031/ja.15890">https://doi.org/10.13031/ja.15890</a></li> <li>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. <a href="https://doi.org/10.13031/ja.15791">https://doi.org/10.13031/ja.15791</a></li> <li>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. <a href="https://doi.org/10.1007/s10546-022-00783-w">https://doi.org/10.1007/s10546-022-00783-w</a></li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol> Extension Publications <ol> <li>Chandel, A.K., Langston, D., 2023. Aerial multispectral imagery for high-throughput mapping of spatial corn yield potentials. VCE Publications, <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.pubs.ext.vt.edu%2Fcontent%2Fpubs_ext_vt_edu%2Fen%2FSPES%2Fspes-526%2Fspes-526.html&data=05%7C02%7Cmbalota%40vt.edu%7Cded81cb83b3846cba17b08dc501768d1%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638473308669072921%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=NuA9J%2BjFYRtaSUfX4tfbNou2zj2oPeSPLdmYyi7jQOs%3D&reserved=0">SPES-526NP</a>.</li> <li>Chandel, A.K., Langston, D., 2023. Aerial multispectral imagery for high-throughput mapping of spatial soybean yield potentials. VCE Publications, <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.pubs.ext.vt.edu%2Fcontent%2Fdam%2Fpubs_ext_vt_edu%2Fspes%2Fspes-527%2FSPES-527.pdf&data=05%7C02%7Cmbalota%40vt.edu%7Cded81cb83b3846cba17b08dc501768d1%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638473308669080111%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=1TeaL1b%2B7gJBTN6p%2BB%2FIY4aU25omORDDRddhY77zEUk%3D&reserved=0">SPES-527NP</a>.</li> <li>Chandel A.K., 2023. Aerial imagery to improve disease diagnosis and management in field crops. VCE Publications, <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.pubs.ext.vt.edu%2Fcontent%2Fpubs_ext_vt_edu%2Fen%2FSPES%2Fspes-515%2Fspes-515.html&data=05%7C02%7Cmbalota%40vt.edu%7Cded81cb83b3846cba17b08dc501768d1%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638473308669087138%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=RE4xLGrXzJgbnVlZvgjK81OfjExOuv8DGUVfqJT9SlA%3D&reserved=0">SPES-515NP</a>.</li> <li>Lee, J. Oakes. Effective Tiller Management for Winter Wheat. 2023. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.pubs.ext.vt.edu%2Fcontent%2Fdam%2Fpubs_ext_vt_edu%2Fspes%2Fspes-431%2FSPES-431.pdf&data=05%7C02%7Cmbalota%40vt.edu%7Cbde88d8d1445400bf1cd08dc54a1e731%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638478301540352300%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Bgu8NWy%2BplLZi%2FSl4B7QB3gLF%2BENgbaM1jisA0vRois%3D&reserved=0">https://www.pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/spes/spes-431/SPES-431.pdf</a></li> <li>Joseph Oakes. Aerial Spectral Imagery to Determine Wheat Fertility Rate and Timing. 2024. <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.pubs.ext.vt.edu%2Fcontent%2Fdam%2Fpubs_ext_vt_edu%2Fspes%2Fspes-582%2FSPES-582.pdf&data=05%7C02%7Cmbalota%40vt.edu%7Cbde88d8d1445400bf1cd08dc54a1e731%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638478301540345015%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=E3VJRAJWIdsxu36s64olErDNbT3d1g9vP17z8lQKyu4%3D&reserved=0">https://www.pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/spes/spes-582/SPES-582.pdf</a></li> <li>Young, P. Ore, S. Hall, 2023.  The Coming Wave of Aquatic Robotics, Resource Magazine, <a href="https://tinyurl.com/4crkbb8j">https://tinyurl.com/4crkbb8j</a></li> <li>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</li> <li>Jackson, J.J., Ladino, K.S. 2024. Drone Sprayer Sizing for Agricultural Applications AEN-174. University of Kentucky Cooperative Extension Service. <a href="https://www2.ca.uky.edu/agcomm/pubs/AEN/AEN174/AEN174.pdf">https://www2.ca.uky.edu/agcomm/pubs/AEN/AEN174/AEN174.pdf</a></li> </ol>

Impact Statements

Working together, in small or large groups, the S-1069 Team made significant progress towards discovery if new aspects of UAS applications in breeding, and agriculture production and natural resources; development of training materials for UAS applications; building platforms for UAS-based data analysis, storage and sharing; and extension and outreach for expansion of UAS-driven agriculture production. The team is leading research using hyperspectral imagery, and machine learning in agricultural research and practice. A common theme across the discussions was the focus on precision agriculture, where tools such as remote sensing, deep learning, and AI are being leveraged to improve crop monitoring, yield prediction, and stress detection. These innovations are being applied to a wide range of crops, including wheat, corn, potatoes, and rice, and are also extending into areas like soil nutrient management, ecological sustainability, and water quality improvement. Several projects emphasized the importance of developing standardized protocols for UAS and the need for robust data management frameworks to handle the growing influx of information from various sensors and imaging systems. Moreover, there is a notable effort to bridge the gap between research and practical application, with many projects involving outreach initiatives to educate the next generation of scientists and practitioners. This collective push is leading to the creation of valuable datasets, software tools, and patents, which are expected to have lasting impacts on both academic research and real-world agricultural practices, driving towards more sustainable and efficient food production systems.