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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

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


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