DEVELOPMENT OF AI-POWERED ROBOTIC HIGH-THROUGHPUT PLANT PHENOTYPING SYSTEMS FOR PRECISION PEANUT BREEDING AND FOREST TREE SEEDLING INVENTORY IN ALABAMA

The over-arching goal of this project is to develop an AI-powered vision-based robotic HTPP framework that is easily adaptable to various crop species (e.g., peanut, pine, soybean and cotton) and to different purposes (e.g., plant breeding, crop scouting, and inventory). Given the economic importance of peanut and pine production in Alabama, the specific objectives of this project are to 1) develop and evaluate an AI-powered vision-based aerial robotic HTPP system to support precision peanut breeding, and 2) develop and evaluate an AI-powered vision-based ground robotic system for automated bareroot pine seedling inventory. The specific aims within Objective 1 are to 1) develop a terrain-following UAV-based multi-modal imaging platform; 2) acquire millimeter spatial resolution multi-modal imagery of peanut plants and ground truth measurements at different growth stages; 3) develop and evaluate an AI-driven image processing pipeline to characterize peanut plant architecture, plant stress, and pod count. The specific aims within Objective 2 are to 1) develop an AI-ready ground-based imaging platform for bareroot pine seedling production fields; 2) develop a deep learning-based video processing pipeline that can perform detection, tracking, root collar diameter estimation, shoot height estimation and health status classification for individual seedlings; 3) evaluate the system performance against year, location, pine species, seedlot, growth stage, and travel direction.