EMERGING PESTS AND DISEASES, ALONG WITH RESOURCE SCARCITY, POSE THREATS TO THE SUSTAINABILITY OF THE U.S. COTTON INDUSTRY. EFFECTIVE DECISION-MAKING FRAMEWORKS BASED ON ACCURATE AND TIMELY INFORMATION ARE NECESSARY TO ADDRESS THESE CHALLENGES. MACHINE VISION TECHNOLOGIES ARE TRANSFORMATIVE IN AGRICULTURE, INCREASING THE SPEED, PRECISION, AND SCALE AT WHICH PLANT TRAITS CAN BE MEASURED AND ANALYZED. THIS PROJECT AIMS TO UPGRADE COTTONSENSE, A HIGH-THROUGHPUT PHENOTYPING TECHNOLOGY DEVELOPED BY TEXAS TECH UNIVERSITY THAT TRACKS, COUNTS, AND DISTINGUISHES VARIOUS COTTON FRUITING STRUCTURES THROUGHOUT THE GROWING SEASON UNDER FIELD CONDITIONS. USING RGB-D CAMERAS ON A GROUND-BASED PLATFORM, COTTONSENSE CAPTURES AND PROCESSES 2D AND 3D DATA, ENABLING DETAILED PHENOTYPIC TRAIT EXTRACTION. THE SYSTEM HAS PROVEN EFFECTIVE IN PRECISELY PREDICTING COTTON YIELDS. OUR GOAL IS TO EXPAND ITS CAPABILITIES TO DETECT PEST AND DISEASE DAMAGE, DIFFERENTIATE COTTON PLANTS FROM WEEDS, IDENTIFY BOLL SIZE AND LOCATION, AND ASSESS LEAF AND CANOPY ARCHITECTURE. ADDITIONALLY, WE WILL DEVELOP A COMPREHENSIVE, CURATED IMAGE DATABASE HOSTED ON AN ACCESSIBLE CLOUD PLATFORM. BY INTEGRATING ADVANCED IMAGING AND DATA ANALYSIS TECHNOLOGIES, THIS PROJECT WILL ENHANCE AGRONOMIC DECISION-MAKING THROUGH EFFICIENT, REAL-TIME CROP MONITORING, ENABLING THE EARLY IDENTIFICATION OF POTENTIAL PEST AND PLANT HEALTH ISSUES BEFORE THEY AFFECT PRODUCTIVITY. THIS PROJECT WILL ALSO ENHANCE THE COTTON BREEDING PROGRAM THROUGH FAST AND DETAILED PHENOTYPING, ENABLING MORE ACCURATE SELECTION OF DESIRABLE TRAITS FOR DEVELOPMENT OF SUPERIOR COTTON VARIETIES. THIS PROJECT REPRESENTS A GROUNDBREAKING APPROACH TO TRANSFORMING AGRICULTURE THROUGH MACHINE VISION APPLICATIONS DEVELOPMENT FOR COTTON.