AI in Agriculture: Innovation and Discovery to Equitably meet Producer Needs and Perceptions
2023 AI Conference • April 17-19, 2023 • Marriott Orlando Airport Lakeside • Orlando, FL
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POSTERS
As part of the poster session, undergraduate and graduate students will participate in a poster competition. The first, second, and third place winners will be awarded $1000, $750, and $500, respectively. The poster competition is sponsored by UF/IFAS Research and winners will be recognized during the closing ceremony.
A listing of the poster presentations for the 2023 AI in Agriculture Conference is displayed below. Posters are sorted by topic category and list the first author as well as their affiliation.
AI Applications in Robotics and Automation
| Poster # | Title | First Author | Affiliation |
| 1 | AI-based Operator-assisted Positioning of Automated Trunk Injection Mechanism using Sensor Fusion | Israel Ojo | University of Florida |
| 2 | Droplet detection and tracking for agricultural spraying systems: A deep-learning approach | Truong Nhut Huynh | Florida Institute of Technology |
| 3 | Selective Harvesting Robots for Cotton Production | Mohd Fazly Mail | Clemson University |
| 4 | Application of artificial intelligence (AI) and robotics for selective cotton defoliation | Jyoti Neupane | Clemson University |
| 5 | An Open Source Commodity AI Drone Platform for High-Resolution Imaging and Precision Location using GPS RTK2 | Devin Willis | University of Florida |
| 6 | Improving the Efficiency of Chemical Spray Systems using Smart Technology for Growth Tracking | Anna Hampton | University of Florida |
| 7 | Fluorescence sensor data as an input to predict sugar content in sugarcane using a machine learning model | Dulis Duron Chevez | Louisiana State University |
| 8 | Individual Animal Identification in Beef Cattle Using Deep Transfer Learning Technology | John Long | Oklahoma State University |
Horticultural Crop Production Supported by AI and Sensing Technologies
| Poster # | Title | First Author | Affiliation |
| 9 | A hyperspectral image analysis pipeline for controlled environment and space agriculture | Stephen Lantin | University of Florida |
| 10 | A web-based system for optimal sensors placement in controlled environment agriculture | Daniel Uyeh | Michigan State University |
| 11 | A novel surrogate model for the structural analysis of a greenhouse based on physics-informed neural networks | Won Choi | Seoul National University |
| 12 | A Digital Collection for Urban Horticulture using Artificial Intelligence and Virtual Realit | Howard Beck | University of Florida |
| 13 | Prediction of Strawberry Vegetative Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods | Caiwang Zheng | University of Florida |
| 14 | Investigation of the relationship between different watering levels and tomato yield using sensor data | Joe Mari Maja | Clemson University |
| 15 | A Strawberry Runner Detection System Using Deep Learning | Mojtaba Ahmadi | California Polytechnic State University |
| 16 | Predicting the Rotation for Sweet Potato Crops in North Carolina | Juliana Pin | North Carolina State University |
| 17 | Predicting the Chemical Composition of grapes using a trained AI model from NIR and RAMAN Data | Azar Alizadeh | University of California, Merced |
| 18 | Sensor Data Fusion and Machine Learning Approach for Pest Infestation Detection in Apples | Akinbode Adedeji | University of Kentucky |
| 19 | Improved Voxel-based Volume Estimation and Pruning Severity Mapping of Apple Trees in the Pruning Period | Kyeong-Hwan Lee | Chonnam National University |
| 20 | Machine Learning-Based Analyses of CO2 Emission from Climate-Smart Sweet Corn Agricultural Systems | Anoop Valiya Veettil | Prairie View A&M University |
AI and Sensing Technologies Applied to Row Crops Management
| Poster # | Title | First Author | Affiliation |
| 21 | Developing root zone soil moisture maps in both high spatial and high temporal resolutions by machine learning | Chi Zhang | University of Florida |
| 22 | Deep-Learning Framework for Optimal Selection of Soil Sampling Sites | Tan-Hanh Pham | Florida Institute of Technology |
| 23 | Autonomous Cross-Platform System for Soil Sampling and Analysis: A Deep Multi-Agent Reinforcement Learning Approach | Godwyll Aikins | Florida Institute of Technology |
| 24 | Forecasting soil temperature using a hybrid artificial neural network model | Golmar Golmohammadi | University of Florida |
| 25 | On-the-go sensing to map soil and nutrient levels and its relationship with corn yield and grain quality | Kabindra Adhikari | USDA-ARS |
| 26 | Canopy cover analysis and analysis of the spatial distribution of edamame | Shikhar Poudel | Virginia Tech |
| 27 | PYCS: Predict Your CropS With Machine Learning | Jonathan Vance | University of Georgia |
| 28 | Improving Grain Yield in Wheat Lines Adapted to the Southeastern United States through Multi Trait and Multi Environment Genomic Prediction Models Incorporating Spectral and Thermal Information. | Jordan McBreen | University of Florida |
| 29 | Soybean yield prediction using machine learning algorithms under a cover crop management system | Leticia Bernabe | Louisiana State University |
| 30 | Machine Learning Algorithms for Fertilizer Application and Corn Cob Quality Prediction Based on Soil Nutrient Data | Binita Thapa | Prairie View A&M University |
| 31 | Predicting grain protein content, size, and yield of malting barley using vegetation indices | Carolina Trentin | Louisiana State University |
| 32 | Predicting Chlorophyll Levels and Biomass Production using Machine Learning and Statistical Approaches | Atikur Rahman | Prairie View A&M University |
| 33 | AI-driven soybean plant density measurement using UAS imagery data | Flávia Luize Pereira de Souza | Louisiana State University |
| 34 | Extracting Crop Model Parameters from Literature using Natural Language Processing | Vijaya Joshi | University of Florida |
AI and Sensing Technologies Applied to Crop Breeding and Pest and Diseases Management
| Poster # | Title | First Author | Affiliation |
| 35 | High-throughput phenotyping (HTP) pipeline by integrating hyperspectral imagery for large winter wheat breeding nurseries | Sehijpreet Kaur | University of Florida |
| 36 | High throughput phenotyping of peanut crops using remote sensing and deep learning techniques | Javier Rodriguez-Sanchez | University of Georgia |
| 37 | Building models to forecast trends in rhisosphere microbiomes | B. Kirtley Amos | North Carolina State University |
| 38 | Leveraging UAS-based hyperspectral images and machine learning in turfgrass breeding | Jing Zhang | University of Georgia |
| 39 | Leveraging UAS-based hyperspectral imagery and data science for cultivar improvement in peanuts | Jerome Maleski | University of Georgia |
| 40 | Quantification of cyst nematode damages caused to soybeans and identifying effective management strategies using aerial multispectral imaging technique | Souradeep Deb | Virginia Tech |
| 41 | Artificial Neural Network Modeling Approach to Estimate Nutritional Quality of Lespedeza Cuneata to Support Small Ruminant Healthy Production. | Sudhanshu Panda | University of North Georgia |
| 42 | Lygus Bug Detection Using Deep Neural Network for Pest Management | Abbas Atefi | California Polytechnic State University |
| 43 | Quantifying Sclerotinia blight severity and effective fungicide application strategies in peanuts using aerial multispectral imaging technique | Jitender Rathore | Virginia Tech |
| 44 | Evaluation of corn leafspot injury and fungicide application impacts using high-resolution aerial multispectral imagery | Sheetal Kumari | Virginia Tech |
| 45 | Automating Severity Assessment of Southern Leaf Blight in Corn Leaves Using Machine Learning | Chanae Ottley | North Carolina State University |
| 46 | Identification of Southern Leaf Blight Infected Corn for Remote-Sensing Field Imagery | Grace Vincent | North Carolina State University |
| 47 | mmLeaf: Leaf Wetness Detection via mmWave Sensing | Maolin Gan | Michigan State University |
| 48 | Using machine learning to improve leaf wetness duration prediction in disease warning systems | Vinicius Andrei Cerbaro | University of Florida |
Hydrology and Environmental Sustainability
| Poster # | Title | First Author | Affiliation |
| 49 | Exploring the potential of hybrid data- and theory-driven hydrological modeling | Young Gu Her | University of Florida |
| 50 | Harnessing Artificial Intelligence for Ecosystem Service Assessments across Scales to Support Sustainable Agriculture | Chang Zhao | University of Florida |
| 51 | Quality Checking of Meteorological Observations used for Disease Alert Systems in Florida | Marcos de Oliveira | University of Florida |
| 52 | Bayesian Calibration Using Data from Impedimetric Biosensors: Predicting E.coli Concentration in Water | Hanyu Qian | University of Florida |
| 53 | Comparing Machine Learning Approaches’ Identification of Key Drivers Influencing Populations of Generic Escherichia coli in Surface Waters in Florida | Kalindhi Larios | University of Florida |
| 54 | Real time stress-risk mapping for agricultural communities: The Precision Agriculture Stress Support (PASS) initiative | Leonardo Mendes Bastos | University of Georgia |
| 55 | AI Model for Assuring Bird Welfare during Transportation | Ramana Pidaparti | University of Georgia |
Digital Agriculture - Adoption and Profitability
| Poster # | Title | First Author | Affiliation |
| 56 | What is farmers perspectives regarding data ownership in digital agriculture? | Songzi Wu | University of Florida |
| 57 | Farmers’ User-experience Related to Digital Advancements in Agriculture | Mehul Bhanushali | Virginia Tech |
| 58 | Agro-Climatic Data by County for Economic Analysis: Geo-aggregation of Rasters with Agricultural Masks | Seong Yun | Mississippi State University |