** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** INFORMATION ON COTTON CROP DEVELOPMENT SUCH AS MATURITY, FRUIT RETENTION PATTERNS, AND PLANT ARCHITECTURE IS LIMITED TO NON-EXISTENT FOR MOST BREEDING AND OFFICIAL VARIETY TRIAL PROGRAMS IN THE U.S. DUE TO THE INFEASIBILITY OF TIME-CONSUMING MANUAL MEASUREMENTS. THE AUTOMATED PHENOTYPING TECHNOLOGIES AND RESULTING BIG DATA WOULD EMPOWER COTTON BREEDERS TO MAKE GREATER GENETIC GAINS AND SHORTEN SELECTION CYCLES. TO ADDRESS THE CHALLENGES FOR RESEARCHERS TO EFFECTIVELY MANAGE AND EFFICIENTLY PROCESS PHENOMICS DATA, THE OVERALL GOAL OF THIS PROJECT IS TO DEVELOP DATA SCIENCE TOOLS AND AI-BASED MODELS TO FACILITATE BREEDING COTTON GERMPLASM WITH IMPROVED IDEOTYPES. SPECIFIC OBJECTIVES OF THE PROPOSED PROJECT ARE TO: 1) MANAGE HETEROGENEOUS FIELD DATA BY DEVELOPING DATA CURATION, STORAGE, AND SHARING WORKFLOWS; 2) DEVELOP REAL-TIME VIDEO TRACKING AND 3D PLANT PART SEGMENTATION DEEP LEARNING MODELS TO MEASURE COTTON ORGAN-LEVEL HIGH RESOLUTION PHENOTYPES; AND 3) VALIDATE AND LEVERAGE PHENOMICS DATA FOR IDEOTYPE BREEDING TO MAXIMIZE COTTON YIELD. SIGNIFICANT IMPROVEMENT ON BREEDING EFFICIENCY WOULD MAKE U.S. COTTON INDUSTRY MORE SUSTAINABLE AND COMPETITIVE IN THE GLOBAL MARKET. THE METHODOLOGIES DEVELOPED IN THIS PROJECT WILL ALSO BENEFIT DATA DRIVEN DECISION SUPPORT SYSTEMS FOR OTHER CROPS AND MAKE THE AI-BASED TOOLS MORE BROADLY AVAILABLE TO CROP BREEDERS AND STAKEHOLDERS. THIS STANDARD RESEARCH PROJECT IS RELATED TO THE PROGRAM AREA DATA SCIENCE FOR FOOD AND AGRICULTURAL SYSTEMS (DSFAS) AND ADDRESSES THE AFRI PRIORITY AREA OF PLANT HEALTH AND PRODUCTION AND PLANT PRODUCTS. IN PARTICULAR, THIS PROJECT FOCUSES ON AI AND MACHINE LEARNING FOR MONITORING, ANALYTICS, AND AUTOMATION IN CROP DEVELOPMENT.