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

(Multistate Research Project)

Status: Active

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SAES-422 Reports

Annual/Termination Reports:

[08/31/2022] [07/24/2023] [09/05/2023] [06/11/2024]

Date of Annual Report: 08/31/2022

Report Information

Annual Meeting Dates: 08/04/2022 - 08/05/2022
Period the Report Covers: 10/01/2021 - 08/05/2022

Participants

Confirmed in-person attendees:

1. Yiannis Ampatzidis (Univ. of Florida)
2. Tom Burks (Univ. of Florida)
3. Dana Choi (Univ. of Florida)
4. Thanos Gentimis (Louisiana State Univ.)
5. Zhen Jia (Univ. of Florida)
6. Edward Kick (North Carolina State Univ.)
7. Juan Landivar (Texas A&M Univ.)
8. Daniel Lee (Univ. of Florida)
9. Amando Lopes de Brito Filho (Louisiana State U.)
10. Yuzhen Lu (Mississippi State Univ.)
11. Henry Medeiros (Univ. of Florida)
12. Brenda Ortiz (Auburn Univ.)
13. Luciano Shiratsuchi (Louisiana State Univ.)
14. Alex Thomasson (Mississippi State Univ.)
15. Gary Thompson (University of Arkansas)
16. Jeffrey Vitale (Oklahoma State Univ.)

Confirmed online attendees:
1. Tom Burks (Univ. of Florida, on Friday)
2. Matt Donovan (AgIntel)
3. Hao Gan (Univ. of Tennessee)
4. Steve Thomson (USDA, NIFA)
5. Paul Weckler (Oklahoma State Univ.)

Brief Summary of Minutes

Aug. 4: Field trip

7:45 – 8:00 am Met at the Hilton hotel parking lot and departed at 8 am for field trip.

8:00 – 8:30 am Traveled to PSREU (Citra, FL) (https://plantscienceunit.ifas.ufl.edu/)

8:30 – 9:00 am Dr. Jim Boyer gave a tour of the PSREU facilities including a detailed explanation of the extensive field trials conducted on-site. Our group engaged in an active discussion with Dr. Boyer regarding various agronomic issues and constraints encountered during field trials.

9:00 – 9:50 am Dr. Congliang Zhou provided a precision ag demonstration of a robot programmed to analyze plant wetness using on-board sensors as well to detect soil mites.

9:50 – 10:00 am Break was provided by PSREU at their main deadquarters.

10:00 – 10:30 am Mr. Whitehurst gave a PowerPoint presentation of his 4,000 acres farm/plantation. This included a video of how Mr. Whitehurst uses aerial drones in his ranching operations to herd cattle remotely. He was assisted by Yilin Zhuang and Stacy Strickland. Mr. Whitehrus also explained how data collected from drones is used to mange his extensive plantation.

10:30 – 11:00 am Traveled to the Whitehurst Cattle Farm in Williston, FL.

11:00 – 11:45 am Mr. Whitehurst gave brief farm tour and a presentation of the various drones he owns and operates. This was followed by an in-field demonstration of Mr. Whitehurst herding his cattle using a drone.

11:45 – 12:15 pm Participants returned to Dept. of ABE in Gainesville, FL.

12:15 – 1:00 pm Lunch.

1:00 – 2:00 pm Meeting with Dr. David Reed of the AI2 Center. Dr. Reed dicussed the new AI initiiatives including the hiring of over 100 new faculty dedicated to AI focused positions. Other issues discussed included the SEC Consortium.

2:00 – 3:00 pm Meeting with Dr. Amber Ross, AI ethics expert. Dr. Ross generated a spritied discussion on the ethics of AI use in agriculture and in general throughout society.

3:00 – 3:30 pm Break and travel to HiPerGator supercomputer facilities on the UF campus.

3:30 – 5:00 pm HiPerGator tour was provided by Dr. Erik Deumens. Participants were allowed access into the Hypergator's complex of servers Dr. Deumens explained hoe Hypergator utilizes the CPU computing power of video cards to process computing tasks. participants also viwed the immense cooling faciliies required by HiPerGator.

5:00 - 7:00 pm Dinner and networking at Mildred’s Restaruant. Meal was sponsored by Auburn University.

Aug. 5: Meeting

7:30 – 7:45 am Meet at the Hilton hotel parking lot

7:45 – 8:00 am Drive to ABE Department on the UF campus.

8:00 – 8:10 am Introduction of the participants including Zoom participants.

8:10 – 8:20 am Dr. Gary Thompson, Executive Director, SAAESD, University of Arkansas. Dr. Thompson provided an overview of multi state Hatch projects incuding how to develop and submit annual reports. Dr. Thompson has provided hisPowerPoint.

8:20 – 8:35 am Dr. Damian Adams, S1090 Administrative Advisor, Associate Dean for Research, UF. Dr. Adams streesed the importance of strengthenin AI in the southern region, which lags behind Corn Belt and Western region.

8:35 – 9:10 am Dr. Steve Thomson, USDA-NIFA (video for funding programs, Q&A via zoom). Dr. Thompsen provided a thorough review of cvarious funding oipportuntiies availabel to AI reserach, including fundamental science based research as well as development and implementation.

9:10 – 9:30 am Dr. Shai Sela, Chief Scientist, Agmatix, Ramat Gan, Israel,gave a presentation of his company's AI technology applications.

9:30 – 9:40 am Coffee break.

9:40 – 11:30 am In the first part of this session, participants were grouped into three teams based on area of expertise to encourage team building and future collaboration. In a follow-up session, groups reconvended to discuss plans for the second project year. Consensus was reached to plan for developing a research proposal to be submitted through an agency such as NSF, USDA, etc. A committee was selected to develop a white paper to begin the proposal writing.

11:30 – 1:00 pm This session was a "working lunch" session to take care of several business items such as electiosn, locations of future meetings, annul reporting etc. The following outcomes were achieved:

Business meeting outcomes:

Elected Yuzhen Lu as our new secretary

LSU was selected as the 2023 meeting location sometine in May 2023.

Jeff Vitale was selected to submit the annual report.

1:00 pm Meeting was adjourned by President Daniel Lee circa 1 pm.

Accomplishments

<h1>Activities (2021-2022)</h1>   <h2 class="x_MsoNormal">Project Level Activities</h2> Members of S1090 project from Auburn University led by Dr. Brenda Ortiz organized a conference targeting undegraduate and young professionals.  A total of 250 participants attened in-person and anothe r150 online. the conference was well received and plans are going forward to hold a similar conference next year. Conference details available online:   Website: <a title="Original URL: https://aaes.auburn.edu/ai-driven-innovations-in-agriculture/. Click or tap if you trust this link." href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Faaes.auburn.edu%2Fai-driven-innovations-in-agriculture%2F&data=05%7C01%7Cjeffrey.vitale%40okstate.edu%7C83af757bee5a44e01b1208da911e60a1%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C637981856910954682%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=G6rexetJ8dAa5fFi5oUF%2BjznH1IK0n6oXxhss4vK0H8%3D&reserved=0" target="_blank" rel="noopener noreferrer" data-auth="Verified" data-linkindex="0">https://aaes.auburn.edu/ai-driven-innovations-in-agriculture/</a> Website with conference posters: <a title="Original URL: https://auburncatalog.instructure.com/courses/1860/pages/conference-posters. Click or tap if you trust this link." href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fauburncatalog.instructure.com%2Fcourses%2F1860%2Fpages%2Fconference-posters&data=05%7C01%7Cjeffrey.vitale%40okstate.edu%7C83af757bee5a44e01b1208da911e60a1%7C2a69c91de8494e34a230cdf8b27e1964%7C0%7C0%7C637981856910954682%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=hziKrx1lRLlVjn9K4f0r4x7TyFNoOl36PP9uCUoUAKc%3D&reserved=0" target="_blank" rel="noopener noreferrer" data-auth="Verified" data-linkindex="1">https://auburncatalog.instructure.com/courses/1860/pages/conference-posters</a>   <h2 class="x_MsoNormal">State Level Activities</h2> Alabama: Auburn University <ol> <li>AI-driven high-throughput phenotyping of agronomic and physiological traits in peanut (Yin Bao)</li> </ol> Weekly UAV-based VNIR hyperspectral imagery data were collected for a F1 peanut population for identifying drought tolerant lines under rainout shelters at the USDA-ARS National Peanut Research Laboratory (NPRL) in Dawson, GA, during pod filling stage in 2021. The project is in collaboration with a peanut breeder (Dr. Charles Chen) and a plant physiologist (Dr. Alvaro Sanz-Saez) from Auburn University and a research chemist (Dr. Phat Dang) from NPRL. Machine and deep learning models were developed to predict three agronomic traits (i.e., pod yield, biomass, and pod count) and two physiological traits (i.e., photosynthesis and stomatal conductance) with reasonable accuracies (R2 values around 0.55). A manuscript has been submitted to Remote Sensing. <ol start="2"> <li>AI-based remote sensing of water quality and HABs for inland water bodies in Southeast (Yin Bao)</li> </ol> A dataset including in-situ chlorophyll a and/or microcystin concentration measurements and Sentinel 2 multispectral satellite imagery has been curated for Lake Okeechobee (FL), Lake Thonotosassa (FL), and Lake Seminole (GA) from 2016 to 2021. LSTM models have been trained and tested to forecast chlorophyll a and/or microcystin concentrations in one month ahead using time-series satellite spectral response. Preliminary results are promising but need further improvement. Continued investigation is needed to see if including other features such as weather parameters can improve prediction accuracy. The developed machine and deep learning models for peanut agronomic and physiological traits prediction will enable screening of a large population for drought tolerance by reducing the labor requirement with traditional phenotyping methods, thus accelerating releasing of climate-smart peanut lines for the Southeast.   Peanut Maturity Assessment: Remote sensing and Artificial Intelligence (Brenda Ortiz) Our research team has begun our work on a project purposed to use remote sensing and AI technology to assist peanut producers in the Southern region in developing improved methods to determine peanut maturity. We are seeking non-desctrucitve methods to assess maturity in cost effective ways to improve peanut harvest and subsequent farm income.  <h4> </h4> Florida: University of Florida Uncertainty-aware Robotic Perception Models for Agricultural Production Systems (Dr. Henry Medeiros) Our team developed a self-supervised machine learning model to detect flowers in images of trees acquired in an orchard. Our algorithm makes it possible to detect individual flowers in real-world conditions without the need for specialized data acquisition systems or training data. An evaluation on publicly available benchmark datasets containing images of multiple flower species collected under various environmental conditions demonstrates that our method substantially outperforms existing techniques, despite the fact that it does not need to be trained using images of the target flower species. A manuscript describing our research findings has been submitted to IEEE Robotics and Automation Letters and is currently undergoing its second round of revisions. Expected Impact(s): The self-supervised machine learning model described above enables the development of systems to detect flowers, fruit, buds, and other relevant plant parts in the field without the need to collect and annotate hundreds to thousands of images reflecting all the potential data acquisition scenarios that may impact algorithmic performance, such as illumination variation and image resolution. Data collection and annotation is currently one of the main factors hindering the application of modern artificial intelligence techniques to agricultural problems. Hence, we expect our model to serve as a foundational architecture for the development of future agricultural robotic perception systems.   Deep Learning Algorithms (Dr. Dana Choi) A deep learning based algorithm was developed to segment green fruits and fruit stems, then the orientation of the fruits were identified to provide guidance for the robotic green fruit system to remove fruits. A path planning algorithm was also developed with a six-degree-freedom robotic arm to engage targeted green fruits. A series of early apple buds images were acquired with two image acquisition systems, and a YOLOv4 model was developed to detect the buds in the tree canopies. Expected impact(s): Machine vision systems are being utilized extensively in agriculture applications. Daytime imaging in outdoor field conditions presents challenges such as variable lighting and color inconsistencies due to sunlight. Motion blur can occur due to vehicle movement and vibrations from ground terrain. A camera system with active lighting can be a solution to overcome these challenges. The developed on-tree apple fruit sizing system with high-resolution stereo cameras and artificial lighting increased performance of fruit sizing compared to manual inspection. Apple fruit size plays an integral role in orchard management decision-making, particularly during chemical thinning, fruit quality assessment, and yield prediction. UAV-based systems for thermal and RGB imaging with machine vision algorithms demonstrated the feasibility of the orchard heating requirement determination methodology, which has the potential to be a critical component of an autonomous, precise frost management system in future studies.   Fruit Based AI Technology (Dr. Daniel Lee) Our team accomplished the following over the past reporting year: <ul style="list-style-type: circle;"> <li>Strawberry plant wetness detection system has been developed using color imaging and deep learning for strawberry production. Based on the 2021-22 results, a portable wetness sensor will be designed for use in commercial strawberry fields.</li> <li>Smartphone-based tool was developed to detect and count two-spotted spider mites (TSSM) on strawberry plants. Various deep learning methods were used to detect TSSM, eggs, and predatory mites. A portable six-camera sensor device was developed and is currently being tested for detecting TSSM in strawberry and almond leaves.</li> <li>Strawberry bruise and size detection systems for postharvest fruit quality evaluation were developed utilizing machine vision and deep learning. These systems can be used in strawberry packinghouses.</li> </ul> Expected impact(s): The plant wetness detection system could enhance the performance of the disease prediction models for strawberry growers in Florida and other parts of the US. The TSSM detection device and tool will increase the efficiency of pest management and thereby increase strawberry yield and profit. The device could be used for other row crops affected by TSSM. The strawberry bruise and size detection system could improve the quality of strawberries.   Computer Algorithms for Machine Vision Applications in Agriculture (Yiannis Ampatzidis) Over the reporting year our team accompllshed the following:  <ul style="list-style-type: circle;"> <li>Strawberry bruise detection system for postharvest fruit quality evaluation was developed utilizing machine vision and deep learning.</li> <li>Disease detection and monitoring system was developed for downy mildew in watermelons utilizing UAV-based hyperspectral imaging and machine learning. This technique was able to classify several severity stages of the disease.</li> <li>Yield and related traits prediction system was developed for wheat under heat-related stress environments. This high-throughput system utilizes UAV-based hyperspectral imaging and machine learning. A yield prediction system was developed for citrus too utilizing UAV-based multispectral imaging and AI.</li> <li>System was developed to determine leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence.</li> <li>Machine vision based system was developed to measure pecan nut growth utilizing deep learning for a better understanding of the fruit growth curve.</li> </ul>   Kentucky (University of Kentucky) Non-Destructive Testing of Fruit in Fod Processing and Manufacturing (Akinbode A. Adedeji) Over the reporting year the following was accompished: <ul style="list-style-type: circle;"> <li>Fundamental understanding of the state-of-the-art in area that relates to the application of nondestructive (NDT) approach (Intelligent sensors) to food (meat, surfaces, and apples) quality evaluation by writing review papers on the subject and published them in high impact journals.</li> <li>Advanced the understanding of application of two sensing methods for qualitative and qualitative assessment of apples and millet cultivars.</li> <li>Developed hyperspectral imaging (HSI) and vibro-acoustic methods for nondestructive testing of apple for codling moth pest. The classification results were well above 90% in both cases for test-set results.</li> </ul>   Expected impact(s): The timely publication of review papers provides a succinct summary of current state of knowledge in these areas that is a resource for many of our colleagues. One of the papers has seen double digit citation in less than a year. Also, some of the results from our work on nondestructive method developments will form the foundation for the application of sensing methods in artificial systems (robotics) development for implementation in the apple and meat processing industries.   Machine Learning Applications in Grape Production (Carlos M. Rodriguez Lopez) Over the reporting year the following was accompished: <ul style="list-style-type: circle;"> <li>Predicting continent and country of origin of vineyard soil samples: We tested the efficiency of 9 different Machine Learning models (i.e., Random Forest, AdaBoost, Bernouli Naïve Bayes, Gradient Boosting Machine, Gausian Naïve Bayes, k-NN(k=5), k-NN(k=10), SVM, and Neural Network) to predict the origin of soil samples using freely available next generation sequencing 16S data from 233 vineyards planted in 5 countries (Australia (n=32), Spain (n=86), Denmark (n=15), Germany (n=10), and USA (n=63)), distributed within 3 different continents (Australia (n=32), Europe (n=138), and North America  (n=63)). The accuracy of the tested models to predict the country of origin varied between 63% and 92% obtained by the Bernouli Naïve Bayes and the Neural Network models respectively. As expected, continent prediction was slightly higher and varied between 69% and 94% obtained by the k-NN(k=10) and the Neural Network models respectively.</li> </ul> <ul style="list-style-type: circle;"> <li>Planted genotype prediction using microbiome data from vineyard soil samples: The same ML models enumerated above were used to predict the planted grapevine genotype (cultivar) using freely available next generation sequencing 16S data from 177 soil samples from vineyards planted with 7 different cultivars in 9 different countries (Cabernet Sauvignon,(n=65; planted in Australia, Spain, South Africa, and USA), Tempranillo (n=60; planted in Spain), Syrah/Shiraz (n=12; planted in Australia, Italy, Spain, and South Africa), Chardonnay (n=12; planted in Argentina and USA), Pinot Noir (n=10; planted in Croacia and USA), Riesling (n=10; planted in Germany) and Solaris (n=10; planted in Denmark). The accuracy of the tested models to predict the country of origin varied between 63% and 81% obtained by the the Bernouli Naïve Bayes and the Neural Network models respectively. All models however showed high levels of variability in their prediction accuracy. We hypothesize that this is due to data imbalance due to the disparity on the number of data sets between cultivars. To test this hypothesis, we will use synthetic and real datasets generated in house.</li> </ul>   Expected impact(s): The quality of grapes used for wine production has been traditionally associated to the concept of Terroir. This concept captures the interaction between the cultivated grapevine variety and the complete natural environment in which a particular wine is produced, including the soil, topography, climate, and the viticultural and oenological practices used to manage the vineyard and during wine production respectively. Recent studies (e.g. Zhou et al. 2021) show that the composition, diversity and function of soil bacterial communities play important roles in determining wine quality which can indirectly affect its economic value. Two of the main drivers of soil bacterial community composition in vineyards are the environmental conditions (Zhou et al. 2021), and the planted grapevine cultivar, suggesting that terroir is not a unidirectional vector, but a feed-back loop between the original soil microbial communities, the vineyard environment, and the planted cultivar. Understanding how the environment and the plant genotype interact to alter the soil microbial communities, is therefore of paramount importance for the elucidation of the elusive concept of terroir.   Mississippi: Mississippi State U. AI Appications in Cotton and Fruit Crops (Alex Thomasson) Over the reporting year our team accompllshed the following:  <ul style="list-style-type: circle;"> <li>Generation of big data sets: <ul> <li>Collected and processed hundreds of soil samples from benchmark soil series in Mississippi in summer 2022.  These samples are being scanned to collect spectra in order to create a dataset that will be used to develop AI-based soil carbon estimations.  (Dr. Nuwan Wijewardane)</li> <li class="x_xxmsolistparagraph">Collected and processed hundreds of images of weeds that are common competitors in cotton crops.  These images are being used to develop AI models that can enable real-time detection of weeds for spot spraying in cotton crops.  (Dr. Yuzhen Lu)</li> </ul> </li> <li>Development of AI-based models for natural resources applications: <ul> <li class="x_xxmsolistparagraph">Used AI on multisource data to forecast groundwater levels in the Mississippi River Valley Alluvial Aquifer.  (Drs. Joel Paz and Mary Love Tagert)</li> </ul> </li> </ul> <ul style="list-style-type: circle;"> <li>Development of AI for image-based detection and classification in the following applications: <ul> <li class="x_xxmsolistparagraph">Cranberry fruit at different maturity levels for soft robotic harvesting.  (Dr. Xin Zhang)</li> <li class="x_xxmsolistparagraph">Treatment of herbicide-resistant weeds in real time, directing an automated tillage implement to reduce herbicide usage and prevent unnecessary soil moisture loss that occur with whole-field tillage.  (Dr. Wes Lowe)</li> <li class="x_xxmsolistparagraph">Separate plastic contaminants from cotton fiber.  (Filip To)</li> <li class="x_xxmsolistparagraph">Locate cotton bolls on plants for robotic harvesting.  (Dr. Alex Thomasson)</li> <li class="x_xxmsolistparagraph">Predict the yield of cotton plants from early-season multisource data including drones.  (Dr. Alex Thomasson)</li> </ul> </li> <ul type="circle"> <li class="x_xxmsolistparagraph">Platic rubbish in cotton fields before harvest, with data from drones. (Dr. Alex Thomasson)</li> <li class="x_xxmsolistparagraph">Volunteer cotton plants in corn and sorghum fields.  (Dr. Alex Thomasson)</li> </ul> </ul> Expected impact(s): Our research is expected to generate impacts from three major areas in the development and application of AI in agriculture: <ol> <li>Generation of big data sets and the enhanced informed decision making capacity that will be unfold.</li> <li>Modeling for natural resource applications to provide stakeholders with more refined, accurate, and wider scoped information from whcich policy and business decisions can be undertaken.</li> <li>The marked advancements in detection and classification of images expanded to new applications will continue to be adopted by producers as a practical tool for real-time automated decision-making and applications.  For example, we have shown that AI can be used for real-time detection of (a) contaminants in cotton fiber, (b) cotton bolls for robotic harvesting, (c) plastic rubbish in cotton fields that can contaminate cotton fiber, and (d) cotton plants that have germinated from seed left in fields at harvest during the prior season, which can serve as a host for pernicious insects. Such improved detection will generate greater productivity and enhanced profits for producers in the Southern region.  </li> </ol>   North Carolina: North Carolina State (Edward Kick)  Our research accompished the foillowing over the past reporting year: <ul style="list-style-type: circle;"> <li>Collected and merged large data sets from published sources such as World Bank, United Nations, Fa. Circa 50 variables were coded using R and Python into an Excel file. 3. Data set was cleaned using R and SPSS, as missing data were searched for missing data that were located, coded, and cleaned using R.</li> <li>Descriptive data analysis was undertaken to identify miscodes, means, standard deviations, etc. Data transformations were applied as necessary in log transformations for skewed data. Replacement data as needed were located, coded, and cleaned in a new access data file.</li> <li>A total of 120 hours of regression analyses were undertaken under the program “structural equation modeling” (SEM) using AMOS. SEM permits tests of hypothesized linkages among all variables, thereby showing the strength of a series of direct and indirect effects. This helps the researchers avoid multicollinearity, which otherwise would compromise the estimations and essentially provide inaccurate inferences. Preliminary results indicate that industrial agriculture results in unsatisfactory consequences for food production. The blind faith that accompanies its usage is seriously questioned for the 134 nations examined. 10. Results and conclusions are published in an agricultural journal. The next set of even more sophisticated results is under examination and very likely to be published in the Swiss journal, Sustainability. These findings are corroborated by Carolan (2016: 112-115).</li> <li>Preliminary literature review of artificial intelligence and agriculture literature undertaken for one section of our recently supported Multistate request.</li> </ul> Expected impact(s): There are said to be four or five nations in the world that use the model of agricultural production that guided much of the Green Revolution (GR). GR is lauded for substantially increasing production of essential agricultural products such as wheat, which helped millions of starving persons in the middle 1900s. However, meta-analyses clearly show the many negative impacts of agricultural production on communities. Our intensive investigation of 134 countries further shows that industrial agriculture has not improved undernourishment in the modern world, and in fact, it has contributed substantially to the degradation of our global environment through production and release. Eco-agricultural farming promises to be a superior alternative, particularly when it is carefully coupled with artificial intelligence. We plan to investigate the attitudes of farming communities, those with a substantial component of farming as we once knew it, to ascertain their views of both Eco-agriculture as explained by us, and artificial intelligence. We have already gathered the base data on communities in every corner of the United States. We have begun to analyze the BIG DATA, which will guide us in the selection of communities for examination.   Oklahoma: Oklahoma State (Brian Arnall, Tyson Ochsner, Jason Warren, and Jeff Vitale) Research over the past reporting year achived the following: <ul style="list-style-type: circle;"> <li>Developed and impmentted an AI algorthm on a data set of winter wheat from nitrogen studies which have been continued for 15 years. Data includes yield, protein, NDVI, soil characteristics, and mesonet wheat and soil data. Results generate yield and various plant growth characteristics. </li> <li>Developed a machine learning model to predict the movement of sugar cane aphid on oklahoma sorghum fields.</li> <li>Developed and applied neural network models for nondestructive estimation of aboveground biomass in rangelands and for high resolution mapping of soil moisture across heterogenous land covers.</li> <li>Aerial sensor data collected on wheat field trials at the Perkins Experiment Station </li> </ul>       South Carolina: Clemson University Our research over the reporting year completed two successive years of hyperspectral data collection of peach leaves at one of Clemson Research Centers – Piedmont Research and Education Center. The data collection started in 2020 and continued until late 2021. Before the pandemic, our team and collaborators from CSIC, Spain collected hyperspectral images at the same peach orchards to determine the effects of Silicon applications on a peach tree and water stress. Young, full-sized leaves with petioles attached were picked for the trees with high and medium K concentrations (45 leaves), while 50 were selected for the peach trees with low K concentrations due to their size. The center research staff working on the same plot designated the plots for high, medium, and low K concentration. The leaves were collected from the midpoint near the base of each tree. The mature leaf samples were then grouped into low, mid, and high K. A snapshot hyperspectral camera was used to scan each leaf of each group before sending it to the Agricultural Services Laboratory for a plant tissue analysis. The spectral data were preprocessed using a calibration panel. Four pretreatment methods (Multiplicative Scatter Effect, Savitzky-Golay first derivative, Savitzky-Golay second derivative and Standard Normal Variate) were applied to the calibrated raw data and partial least square (PLS) was used to develop a model for each pretreatment. Expected impact(s): The impact of the K prediction project on the peach tree is twofold, 1 -helps determine the spectral signature where K can be predicted, and 2—use of pretreatment methods significantly improves the development of PLS models. The results of this work open the possibility of developing a more miniature detector of K, which only uses the essential bands. It will also facilitate the development of sensors specific to K detection which will be cheaper for farmers to use.   Tennessee: University of Tennessee The work conducted by Tennessee focused on the development of AI technology for the improvement of livestock and poultry health and welfare. Our research team developed and evaluated ancamera system in the lab and in the broiler farm for automated gait score assessment. The outcome of this project is an automated tool that helps broiler farmers to identify lameness in broilers early. It provides timely information for broiler farmers to improve their farm management practices for better animal welfare and higher production.multiple research and commercial broiler farms in the U.S. Expected impact(s): In this project, a computer vision system was developed to provide an automated assessment of broiler gait scores in commercial farms. The system was low cost, required minimum maintenance, and was designed to be used for most commercial broiler farms. The potential impact of the research is to provide farmers with an automated tool for accurate and timely broiler welfare evaluation. It will lead to improvements in farm management, thus, improvements in animal welfare and health. It will eventually help improve animal productions and will also bring in economic benefits to US agriculture and food systems.     Texas: Texas A&M (Juan Landivar) 1. Crop Phenotyping: Cotton and Wheat Cotton: We developed an Unmanned Aircraft System (UAS) based High Throughput Phenotyping (HTP) pipeline that can measure temporal growth and spatial development parameters for cotton. The system includes a big data management system (CottonHub) which facilitates UAS data management (search, upload, and download), generates geospatial data products for visualization purposes, extracts plant growth features, and communicates with users and cooperators. The system includes tools to perform growth analysis of the experimental units or genotypes and extract approximately twelve growth parameters depicting the characteristics and performance of the genotypes in field conditions. Wheat: We are participating in a wheat CAP grant as part of a team of 19 wheat programs in the USA. Our work involves developing a standardized UAS data collection protocol for high-quality data collection, training the WheatCAP team members in UAS data collection, management/processing of the UAS data collected by the national wheat breeding programs, providing visualization tools to the WheatCAP team members, and extracting plant growth features to select elite germplasm. Expected impact(s): The phenotyping systems described above are used by cotton and wheat breeders to manage, display, and analyze phenotypic features of experimental genotypes. The system is included in the NSF grant led by Drs. Hombing Zhang, Wayne Smith, and Steve Hague (Texas A&M University, cotton Breeders), and other breeders from across the Cotton Belt. The cotton UASHub has the potential to save cotton breeders as much as 60% of the cost of collecting phenotype and yield data from field plots. Similarly, the WheatCAPHub (https://wheatcap.uashubs.com) is part of a USDA-NIFA grant (funded, $15M), led by Dr. Amir Ibrahim (Texas A&M Wheat Breeder). Our contribution has the potential of similar cost reductions to the wheat breeder, in addition to facilitating communication among scientists from across the country.   2. Testbed Data Management and Uses We completed four years (2019 to 2022) of UAS data collection in a large (approximately 100 acres) commercial cotton field located in Driscoll, Texas as a testbed. RGB and multispectral data were collected on a weekly basis from UAS. The collected data were processed to generate fine spatial and high temporal resolution orthomosaic and Digital Surface Models (DSM). The testbed fields were divided into grids of 10 x 10 m (100 m2) which resulted in a total of approximately 4,000 grids within the testbed. Plant features extracted from each grid include: Canopy Cover (%), Plant Height (m), Canopy Volume (m3), and Vegetative Indexes (Excess Greenness Index and NDVI). Seedcotton yield was obtained from the yield monitoring system of the cotton harvester. The data was used to develop Digital Twin models for in-season crop management and to obtain an early-season estimation of cotton yield for marketing purposes. Expected impact(s): The digital twin system developed from the testbed data described above was used to estimate crop termination time and defoliant rates during the 2021 and 2022 seasons. Canopy Cover data was used to create management zones for the precision application of defoliants. The system accurately estimated the time of defoliation as early as one month before the event. The digital twin model estimated cottonseed yield within 5% of the actual yield approximately a month before harvest. It is expected that the prescription crop management package along with in-season yield forecasting capabilities developed from the testbed data would optimize production cost, yield, and fiber quality. Having earlier and more accurate forecasts of pre-harvest yield would be extremely useful in facilitating forward selling from growers to merchant buyers. Thus, a reduction in production cost along with enhanced marketing strategies could enhance the profitability of cotton production by approximately 10%. 3. Extension & Outreach Cotton cultivar tests are crucial educational activities of research and extension agronomists, but their establishment, maintenance, and data collection are time-consuming and costly. Although the information generated is valuable, the field plots are seldom visited by producers. This project proposes to bring the field plots to producers via a web-based platform designed to analyze and visualize the growth characteristics of cultivars and to make comparisons between entries. A cotton cultivar data management web-based Hub (CultivarHub) was developed to facilitate the communication between extension personnel (crop specialists and county agents) with producers. Expected impact(s): The impact of the CultivarHub is twofold; (1) help cotton specialist manage, analyze, and summarize data from cultivar, agrochemicals, or irrigation evaluation trials, and (2) facilitate the communication and educational outreach of extension specialist, county agents, or IPM agents with producers. It is expected that this web-based platform can increase the outreach and technical communication efficiency of extension personnel by 60%.  Extension presentation(s): Landivar J, Mahendra Bhandari, Josh McGinty, Murilo Maeda, Jose Landivar, Hend Alkittawi, Anjin Chang, Daniel Gonzales. 2022. UAS-Based Platform for Evaluating the Performance of Cotton Cultivars for Research and Outreach. 2022 Beltwide Cotton Conferences, San Antonio, Texas. January 7, 2022.

Publications

Alabama: Aubrun University  Citation of the conference proceedings paper: Oliveira, M.F., F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco. 2022. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target Regression. In Proceedings of the 2022 International Conference of Precision Agriculture. June 26-29, 2022 Minneapolis.   Florida: University of Florida Yuan, W., Choi, D., Bolkas, D., Heinemann, P.H. and He, L., 2022. Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification. International Journal of Remote Sensing, 43(8), pp.3106-3130. Yuan, W., Choi, D., & Bolkas, D. (2022). GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees. Computers and Electronics in Agriculture, 197, 106966. Zahid, A., Mahmud, M.S., He, L., Schupp, J., Choi, D. and Heinemann, P., 2022. An Apple Tree Branch Pruning Analysis. HortTechnology, 32(2), pp.90-98. Zhang, H., He, L., Di Gioia, F., Choi, D., Elia, A. and Heinemann, P., 2022. LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato. Smart Agricultural Technology, 2, p.100053. Mahmud, M.S., Zahid, A., He, L., Choi, D., Krawczyk, G. and Zhu, H., 2021. LiDAR-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers. Computers and Electronics in Agriculture, 191, p.106565. Patel, A., W. S. Lee, N. A. Peres, and C. W. Fraisse. 2021. Strawberry plant wetness detection using computer vision and deep learning. Smart Agricultural Technology 1, 2021, 100013, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.100013 Yun, C., H.-J. Kim, C.-W. Jeon, M. Gang, W. S. Lee, and J. G. Han. 2021. Stereovision-based ridge-furrow detection and tracking for auto-guided cultivator. Computers and Electronics in Agriculture 191, 2021, 106490, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2021.106490. Puranik, P., W. S. Lee, N. Peres, F. Wu, A. Abd-Elrahman, and S. Agehara. 2021. Strawberry flower and fruit detection using deep learning for developing yield prediction models. In the Proceedings of the 13th European Conference on Precision Agriculture (ECPA), July 19-22, 2021, Budapest, Hungary. Zhou, X., W. S. Lee, Y. Ampatzidis, Y. Chen, N. Peres, and C. Fraisse. 2021. Strawberry maturity classification from UAV and near-ground imaging using deep learning. Smart Agricultural Technology 1, 2021, 100001, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.10000 Costa L., McBreen J., Ampatzidis Y., Guo J., Reisi Gahrooei M., Babar A., 2021. Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes. Precision Agriculture, 23 (2), 622-642. Costa L., Ampatzidis Y., Rohla C., Maness N., Cheary B., Zhang L., 2021. Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Computers and Electronics in Agriculture, 181, 105964, <a href="https://doi.org/10.1016/j.compag.2020.105964">doi.org/10.1016/j.compag.2020.105964</a>. Costa L., Archer L., Ampatzidis Y., Casteluci L., Caurin G.A.P., Albrecht U., 2021. Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence. Precision Agriculture 22, 1107-1119, <a href="https://doi.org/10.1007/s11119-020-09771-x">https://doi.org/10.1007/s11119-020-09771-x</a>. Nunes L., Ampatzidis Y., Costa L., Wallau M., 2021. Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Computers and Electronics in Agriculture, 183, 106080, <a href="https://doi.org/10.1016/j.compag.2021.106080">doi.org/10.1016/j.compag.2021.106080</a>. Vijayakumar V., Costa L., Ampatzidis Y., 2021. Prediction of citrus yield with AI using ground-based fruit detection and UAV imagery. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, 2100493, doi:10.13031/aim.202100493. Zhou, C., W. S. Lee, O. E. Liburd, I. Aygun, J. K. Schueller, and I. Ampatzidis. 2021. Smartphone-based tool for two-spotted spider mite detection in strawberry. ASABE Paper No. 2100558. St. Joseph, MI.: ASABE. Zhou, X., Y. Ampatzidis, W. S. Lee, and S. Agehara. 2021. Postharvest strawberry bruise detection using deep learning. ASABE Paper No. 2100458. St. Joseph, MI.: ASABE. Influence of Planting Date, Maturity Group, and Harvest Timing on Soybean (Glycine max (L.) Yield and Seed Quality, PRISCILA CAMPOS, DONNIE MILLER, JOSH COPES, MELANIE NETTERVILLE, SEBE BROWN, TREY PRICE, DAVID MOSELEY, THANOS GENTIMIS, PETERS EGBEDI, RASEL PARVEJ3 (Accepted by Crop, Forage, & Turfgrass Management, Summer 2022). In this paper, modern methodologies were implemented in the analysis of the results, as well as more traditional statistical techniques. The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle, BARBOSA JÚNIOR, M.R.; TEDESCO, D.; CARREIRA, V.S.; PINTO, A.A.; MOREIRA, B.R.A.; SHIRATSUCHI, L.S.; ZERBATO, C.; SILVA, R.P., Drones 2022, 6, 112. https://doi.org/10.3390/drones6050112 UAVs to Monitor and Manage Sugarcane: Integrative Review, BARBOSA JÚNIOR, M.R.; MOREIRA, B.R.A.; BRITO FILHO, A.L.; TEDESCO, D.; SHIRATSUCHI, L.S.; SILVA, R.P., Agronomy 2022, 12, 661. https://doi.org/10.3390/agronomy12030661 Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data, TEODORO, P. E.; TEODORO, L. P. R.; BAIO, F. H. R.; SILVA JUNIOR, C. A.; SANTOS, R. G.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; OSCO, L. P.; GONCALVES, W. N.; CARNEIRO, A. M.; MARCATO JUNIOR, J.; PISTORI, H.; SHIRATSUCHI, L. S., Remote Sensing, v. 13, p. 4632, 2021 Comparison of Machine Learning Techniques in Cotton Yield Prediction Using Satellite Remote Sensing, MORELLI-FERREIRA, F.; MAIA, N.J.C.; TEDESCO, D.; KAZAMA, E.H.; MORLIN CARNEIRO, F.; SANTOS, L.B.; SEBEN JUNIOR, G.F.; ROLIM, G.S.; SHIRATSUCHI, L.S.; SILVA, R.P. Preprints 2021, 2021120138 (doi: 10.20944/preprints202112.0138.v2). Published and in preparation for Remote Sensing.     Kentucky: University of Kentucky Ekramirad, N., Khaled, Y.A., Doyle, L., Loeb, J., Donohue, K.D., Villanueva, R., and Adedeji, A.A. (2022). Nondestructive detection of codling moth infestation in apples using pixel-based NIR hyperspectral imaging with machine learning and feature selection. Foods 11(8), 1 - 16. (Citation: 2) Rady, A., Watson, N., and Adedeji, A.A. (2021). Color imaging and machine learning for adulteration detection in minced meat. Journal of Agriculture and Food Research 6(100251), 1-11. (Citation: 1) Watson, N.J., Bowler, A.L., Rady, A., Fisher, O.J., Simeone, A., Escrig, J., Woolley, E., and Adedeji, A.A. (2021). Intelligent sensors for sustainable food and drink manufacturing. Frontiers in Sustainable Food Systems. 5, 642786. (Citation: 5) Ekramirad, N., Al Khaled, Y.A., Donohue, K., Villanueva, R., Parrish, C.A., and Adedeji, A. (2021). Development of pattern recognition and classification models for the detection of vibro-acoustic emissions from codling moth infested apples. Postharvest Biology and Technology 181, 111633. (Citation: 1) Khaled, Y.A., Parrish, C. and *Adedeji, A.A. (2021). Emerging nondestructive approaches for meat quality and safety evaluation. Comprehensive Reviews in Food Science and Food Safety. 20(4): 3438-3463. (Citation: 15)   Mississppi: Mississippi State University Chen, D., Lu, Y., Li, Z., and Young, S.  2022.  Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems.  Computers and Electronics in Agriculture. Yadav, P.K., Thomasson, J.A., Hardin, R.G., Searcy, S.W., Braga-Neto, U., Popescu, S.C., Martin, D.E., Rodriguez III, R., Meza, K., Enciso, J. and Solorzano, J.  2022. Volunteer cotton plant detection in corn field with deep learning.  In Proc. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII (Vol. 12114, pp. 15-22). SPIE.   North Carolina: NC State   Edward L. Kick, Laura McKinney, Steve McDonald, Andrew Jorgenson. “A Multiple-Network Analysis of the World System of Nations” is printed in Chinese and is used in what we hope will be a forthcoming publication in Sustainability.” That paper introduces the possibility of using artificial intelligence in farming around the world. Edward L. Kick, Gregory Fulkerson, and Ahad Pezeshkpoor. “Agriculture Grains, and Beef Production: Remedies for food for Food Insecurity and the Ecological Footprint When the Cataclysm Comes?” Agricultural Research and Technology 23: 53-57. Edward L. Kick “Cross-National Empirical Studies of Sustainability, Agriculture and the Environment: Cumulating Forward or Erring in an About Face?” Agricultural Research and Technology 25: 601-603. Edward L. Kick. “Taking a World View”. College of Agriculture and Life Sciences Newsletter. Edward L. Kick and Ahad Pezeshkpoor. “Biomes, World-System Positions, and National Characteristics as linked Precursors to Global Undernourishment and the Ecological Footprint” Under revision for publication in Sustainability.     South Carolina: Clemson University Abenina MIA, Maja JM, Cutulle M, Melgar JC, Liu H. Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis. AgriEngineering.2022;4(2):400-413. https://doi.org/10.3390/agriengineering4020027   Tennessee; University of Tennessee Nasiri, A., Yoder, J., Zhao, Y., Hawkins, S., Prado, M., & Gan, H. (2022). Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Computers and Electronics in Agriculture, 197, 106931.   Texas: Texas A&M Bhandari, M.; Baker, S.; Rudd, J. C.; Ibrahim, A. M. H.; Chang, A.; Xue, Q.; Jung, J.; Landivar, J.; Auvermann, B. Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (Uas)-Based Phenotyping. Remote Sens. 2021, 13 (6). https://doi.org/10.3390/rs13061144. W. Wu, S. Hague, J. Jung, A. Ashapure, M. Maeda, A. Maeda, A. Chang, D. Jones, J.A. Thomasson, J. Landivar, "Cotton row spacing and Unmanned Aerial Vehicle sensors," Agronomy Journal, https://doi.org/10.1002/agj2.20902, 2021 A. Chang, J. Jung, J. Landivar, J. Landivar, B. Barker, R. Ghosh, "Performance evaluation of parallel structure from motion (SfM) processing with public cloud computing and an on-premise cluster system for UAS images in agriculture," International Journal of Geo-Information, 10, 677, https://doi.org/10.3390/ijgi10100677, 2021 S. Oh, A. Chang, A. Ashapure, J. Jung, N. Dube, M. Maeda, D. Gonzalez, J. Landivar, "Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework", Remote Sensing, 12(18):2981, DOI: 10.3390/rs12182981, 2020 M. Bhandari, A. Ibrahim, Q. Xue, J. Jung, A. Chang, J. Rudd, M. Maeda, N. Rajan, H. Neely, J. Landivar, "Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)", Computers and Electronics in Agriculture, 176:105665, DOI: 10.1016/j.compag.2020.105665, 2020 Landivar J., J. Jung, A. Ashapure, M. Bhandari, M. Maeda, J. Landivar, A. Chang and D. Gonzalez. 2021. In-Season Management of Cotton Crops Using “Digital Twins” Models. ASA-CSSA-SSSA International Annual Meeting, Salt Lake City, UT, November 9-11, 2021. Landivar J, M. Maeda, A. Chang, J. Jung, J. McGinty, C. Bednarz, 2021. "Estimating the time and rate of harvest aid chemicals using an Unmanned Aircraft System," 2021 Beltwide Cotton Conferences, Online Conference, January 5 - 7, 2021 Ashapure A., J. Jung, A. Chang, S. Oh, J. Yeom, M. Maeda, A. Maeda, N. Dube, J. Landivar, S. Hague, W. Smith, 2020. "Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 180-194. J. Jung, M. Maeda, A. Chang, M. Bhandari, A. Ashapure, J. Landivar, "The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems", Current Opinion in Biotechnology, vol. 70, pp. 15-22, 2021

Impact Statements

Our first year of project activities has had positive impacts on Southern regional agriculture through a diverse range of AI activities as detailed in the Accomplishments section of this report. Our accomplishments include advances in row crop production, fruit production and harvesting, and in food processing and manufacturing.

Date of Annual Report: 07/24/2023

Report Information

Annual Meeting Dates: 04/17/2023 - 04/19/2023
Period the Report Covers: 09/01/2022 - 04/17/2023

Participants

Please see attached below

Participants Attachment:

Contacts_Sp2023.xlsx

Brief Summary of Minutes

Minutes Attachment:

S1090_Spring2023_MeetingRecap.docx

Accomplishments

Publications

Impact Statements

Date of Annual Report: 09/05/2023

Report Information

Annual Meeting Dates: 08/09/2023 - 08/11/2023
Period the Report Covers: 09/01/2022 - 01/01/2023

Participants

Participants Attachment:

Participants_LA.docx

Brief Summary of Minutes

Duirng the meeting, we had the opportunity to discuss various collaborative efforts, that culminated to a google document spreadsheet with multiple participants "buying in to various projects"

The first day of the conference was dedicated to showcasing the use of AI in agriculture to a specialty crop that is important for Louisiana (sugarcane). The participants had the opportunity to visit the John Deere factory at Thibodaux and discuss cutting edge technology initiatives with the technical lead team there. The participants also got to tour the facility on site. The second stop was the LSU Ag Center Sugarcane Station, where the participants got to see first hand the use of AI to improve the Sugarcane Breeding program in Louisiana. The team there presented us with the whole process, and showed how an AI powered app is fundamental in their new breeding scheduling.

The second day of the conference was dedicated to creating possible collaborations, both for common papers and common grant writting.

Minutes Attachment:

Agenda004.docx

Accomplishments

Publications

Impact Statements

Date of Annual Report: 06/11/2024

Report Information

Annual Meeting Dates: 04/15/2024 - 04/17/2024
Period the Report Covers: 04/15/2024 - 04/17/2024

Participants

Juan Landivar
Gary Thompson
Alex Thomasson
Ioannis Ampatzidis
Damian Adams
Jeffrey Vitale
Thanos Gentimis
Mahendra Bhandari

Brief Summary of Minutes

The S_1090 mid year meeting took place at the 2024 "AI in Agriculture" conference at College station, TX. It was called to order at 10 am on Monday April 15, 2024, by Jeffrey Vitale.

The main speaker for the two hour meeting was Gary Thompson. Two main topics were discussed. The first was the weak annual reporting that the S_1090 project has been submitting. Dr. Thompson presented comments from external reviewers that criticized the lack of detail and insuffienct attention to how the project is meeting objectives. In general, the problem is that participants have been reporting state-level Hatch project acomplishments rather than satisfying the multi-state objectives. It was decided to devote significant time during the upcoming annual meeting (Aug 8-9, 2024) to draft the 2024 annual report. This will include having participants provide their acomplishemnts during the meeting to filter state-level Hatch activities from the multi-state. A two hour time slot has already been scheulde for the upcoming meeting.

The second order of business was to choose the venue for the upcoming 2025 "AI in Agriculture" conference. Prior to th emeeting, there was a movement to have Washington State host the meeting. After discussion, it was decided that their participation in the S_1090 has not been active enough to warrant this choice. Afer discussing for 45minutes, it was decided to choose between either Mississippi State or North Carolina State as potential hosts for the 2025 conference venue. There was also the decision to put forth a formal committee to plan the annual conference. This would include the hosts of the upcoimng conference serving as the primary planners, but the committee will also include hosts for the following year. This will give them them experince in learning how to organize and plan the conference.

The meeting was unanimously adjourned at 12 noon by participants. After the meeting , the S_1090 participants continued to discuss project issues with one another.

Introduction

The Annual AI in Agriculture Conference, sponsored by the United States Department of Agriculture (USDA), has rapidly become a cornerstone for advancing the application of artificial intelligence (AI) in the agricultural sector. This annual conference, which began in 2022 in Auburn, has shown significant growth and impact in its brief history. It brings together experts, researchers, and practitioners from multiple land-grant universities across the Southern United States to explore and exchange cutting-edge ideas and technologies that aim to revolutionize agricultural practices through AI. The conference is chaperoned by the S1090 multistate project AI: In Agroecosystems: Big Data and Smart Technology-Driven Sustainable Production, which has grown in parallel with the conference. In the following paragraphs we will give an overview of both, explaining their importance and contribution to the field.

Conference History:

2022 Inaugural Conference: Laying the Foundation

The inaugural session of the conference held in Auburn laid a solid foundation with 120 participants marking the beginning of a southern consortium focused on integrating AI into agriculture. The conference highlighted initial research focuses such as predictive analytics for crop and soil management, AI-driven pest control solutions, and automation technologies for improved agricultural productivity. This first meeting was crucial for setting the stage, defining the roadmap, and fostering collaborations among leading land-grant universities. The conference also introduced practical workshops where participants could gain hands-on experience with AI tools and technologies, which were highly appreciated for their immediate applicability.

2023 Expansion: Widening the Scope and Reach

In 2023, the conference was held in Florida, seeing a significant increase in participation to 200 attendees. This session expanded the scope of discussions to include AI applications in climate resilience, water resource management, and sustainable farming practices. It underscored the importance of AI in managing large datasets for real-time decision making and precision agriculture.

2024 Milestone: Record Participation and Diverse Innovations

The latest conference in Texas marked a new milestone with 320 participants, showcasing the growing interest and investment in AI-driven agricultural innovations. The 2024 conference not only addressed advanced AI applications in optimizing farm machinery, enhancing livestock management, and automating food systems but also explored the socio-economic impacts of AI, such as job creation in tech-driven farming and ethical considerations in data usage. This year’s meeting demonstrated significant progress in turning research into actionable solutions that attendees were eager to implement.

Review:

Over the years, the conference has been pivotal in introducing and discussing various AI technologies. Key advancements include the development of AI models that predict crop yields more accurately and earlier in the season, drones equipped with imaging sensors to monitor crop health, and robotic systems for harvesting. The integration of these technologies aims to reduce labor costs, increase precision in pesticide application, and enhance overall farm efficiency.

A notable outcome of these conferences has been the initiation of several collaborative projects and research initiatives. These projects leverage the collective expertise and resources of the participating universities to tackle large-scale challenges that no single institution could handle alone. Topics such as genetic crop improvement, pest migration patterns, and water use efficiency have benefited from such collaborative efforts, driving forward the research frontier in agricultural sciences.

The involvement of industry partners has also been a critical component of the conference’s success. Companies specializing in AI, robotics, and agricultural technology have found the conference to be an excellent platform for showcasing their latest products and for scouting new ideas and talents. This industry-academia partnership is vital for translating research insights into market-ready products and services that can significantly impact the agricultural sector.

Each conference iteration places a strong emphasis on education and workforce development. Through workshops, seminars, and panel discussions, participants, including students and early-career researchers, are educated on the latest AI tools and techniques, ethical issues in AI, and the future of agricultural jobs. These educational activities are crucial for preparing a tech-savvy workforce ready to implement and innovate within the AI-agriculture nexus.

As the conference looks to the future, several challenges and opportunities remain. Key among these is the need to enhance AI interpretability and trust among farmers, integrate more comprehensive data sets for AI models, and address the digital divide that could hinder technology adoption. Future conferences will likely focus more on these aspects, along with continued exploration of AI’s role in climate change mitigation and adaptation in agriculture.

Panels:

Water Resource Management Panel

One of the critical focuses of this years’ conference was the Water Resource Management Panel, which convened experts in hydrology, climatology, and agricultural planning. This panel addressed the pressing issues of water scarcity and efficiency in irrigation practices, crucial under the growing strain of climate variability. Innovations discussed included AI-based predictive models for water demand forecasting and optimization algorithms for irrigation systems that significantly reduce water waste while maintaining crop health. The panel also explored the implications of regulatory policies on water resources, emphasizing the need for synergy between technological advancements and sustainable water governance. This session was instrumental in highlighting AI's potential to enhance water use efficiency in agricultural practices, making it a cornerstone for future conferences.

Industry Expert Panel

The Industry Expert Panel brought together leaders from tech giants and startups within the agricultural technology sector to discuss the future trajectory of AI in farming. This panel provided insights into the latest technological advancements, such as machine learning models that improve pest detection and drones that optimize seed planting patterns. Industry representatives shared case studies where AI integration had led to tangible benefits, including increased yields and reduced operational costs. Furthermore, this dialogue fostered a critical discussion on the barriers to adopting these technologies, primarily focusing on the economic and infrastructural challenges faced by the agricultural community. The panel concluded with a commitment to closer collaboration between tech companies and farming professionals to tailor AI solutions that are accessible and beneficial to all farmers.

Farmers' Panel

Perhaps the most impactful session was the Farmers' Panel, which directly involved the end-users of agricultural AI technologies—the farmers. This panel provided a platform for farmers to voice their experiences, concerns, and the practical impact of AI on their farming operations. Topics of discussion included the usability of AI tools in everyday farming activities, the economic impact of AI investments, and the cultural shifts required within farming communities to embrace such technologies. Farmers shared success stories of using AI to enhance crop diagnostics and yield predictions, which have led to better crop management and reduced waste. This session was crucial for technology developers and researchers to receive grounded feedback, ensuring that future AI innovations are user-centric and address the real-world challenges of farmers. The line: “AI will not replace a human, but a human with AI will replace a human without one” coined by one of the panelists completely encapsulates the trajectory of our field.

These panels not only highlighted the diverse applications of AI in agriculture but also fostered a multi-stakeholder dialogue that is essential for the holistic adoption of technology in this traditionally conservative field. Each panel, by focusing on different aspects of the agricultural industry, helped to paint a comprehensive picture of the challenges and opportunities presented by AI, guiding the pathway for future research and implementation strategies.

Poster Sessions

The poster sessions at the conference have consistently been a highlight, offering a dynamic forum for both budding and established researchers to display their findings. In its inaugural year in Auburn, the focus of these sessions was largely introductory, designed to educate and inform participants about the fundamental aspects of artificial intelligence in the context of agriculture. Researchers presented on a range of topics from basic AI principles and data handling to preliminary applications in monitoring soil health and crop conditions. These early presentations played a crucial role in setting the educational tone for the conference, helping to align the varied expertise of participants towards a common understanding of AI’s potential in agriculture. They served as a springboard for further exploration and set the groundwork for more advanced applications, facilitating a shared baseline from which all attendees could progress.

By the 2024 conference in Texas, the evolution of the poster sessions mirrored the overall growth and deepening focus of the conference itself. The number of presentations had expanded dramatically, with more than 50 posters illustrating sophisticated uses of AI across a broad spectrum of agricultural needs. These included advanced automation systems that integrate drones and robotic technologies for precision farming, and machine learning models that enhance predictive analytics for crop yield and detect plant diseases early. The range of topics showcased a significant shift towards the implementation of complex AI solutions tailored to specific agricultural challenges, reflecting a move from theoretical to practical, impact-driven research. This maturation in the content offered not only provided new insights and knowledge but also highlighted the practical benefits and improvements AI technologies are beginning to bring to the agricultural sector. The enthusiastic participation and the quality of research presented underscored the vibrant, innovative spirit that defines the S1090 conference, marking it as a seminal event in the field of AI in agriculture.

Invited Speakers

The conference has consistently attracted top-tier talent and experts in the field of AI and agriculture, with invited speakers from globally recognized corporations such as IBM, Microsoft, NVIDIA, John Deere, and more. These industry leaders brought with them insights into the cutting-edge applications of AI technologies that are currently being developed or implemented. For instance, speakers from NVIDIA discussed advancements in GPU-accelerated computing that facilitate deep learning models capable of analyzing vast amounts of agricultural data in real time. Meanwhile, representatives from John Deere showcased the latest in farm machinery automation, which incorporates AI to optimize planting and harvesting operations. The presence of these high-caliber speakers not only elevated the conference’s prestige but also enriched the learning experience for all attendees, providing them with a glimpse into the future of technologically driven agriculture.

The contributions from such esteemed corporate speakers were instrumental in highlighting the innovative, practical applications of AI that their companies are pioneering. A speaker from IBM explored the integration of AI with weather prediction models to improve crop yield predictions and manage risks associated with climate variability. Microsoft experts provided insights into cloud computing infrastructures that support AI algorithms in processing agricultural data more efficiently and securely. These sessions not only offered theoretical knowledge but also practical strategies that participants could consider implementing in their own agricultural practices.

A notable highlight was the return of a speaker from the first conference, who remarked on the substantial growth and evolution of the event over the years. He pointed out that what started as a regional meeting has matured significantly to be considered a national event, indicative of its expanding influence and the critical role it plays in shaping the future of agriculture in the United States. This sentiment was echoed by many participants who appreciated the increasingly diverse topics and the inclusion of more complex discussions surrounding the ethical, economic, and social implications of AI in agriculture. The conference's evolution reflects its success in fostering a comprehensive dialogue that not only addresses the technical aspects of AI but also its real-world impacts, positioning the conference as a pivotal, nationally recognized platform for future innovations in agricultural technologies.

Conclusion

The S1090 Multistate Project and its corresponding AI in Agriculture annual conference has made commendable progress in fostering innovation, collaboration, and education among the southern land-grant universities. As it grows, the conference not only serves as a beacon of knowledge and innovation but also as a catalyst for tangible improvements in agricultural practices through AI. The continued success of this conference promises to usher in a new era of agriculture that is smarter, more efficient, and sustainable, benefiting stakeholders across the spectrum.

Accomplishments

The major accomplishment was to put in place a more organized effort to write annual reports in response to recent feedback.

Publications

Impact Statements

The meeting developed a new protocol for the planning of the "AI in Agriculture" conference that will strengthen them into the future.