THIS PROJECT ADDRESSES THE NEED FOR A METHOD OF ACQUIRING HIGH-THROUGHPUT FORAGE COMPOSITIONAL ATTRIBUTE DATA TO ACCELERATE IMPROVEMENTS IN FORAGE CULTIVARS. IMPROVED FORAGE CULTIVARS PROVIDE ECONOMIC OPPORTUNITIES FOR LIVESTOCK AND CROP FARMING OPERATIONS AND PROMOTE A MORE STABLE, SUSTAINABLE AGRICULTURE. HOWEVER, THE EXISTING LABORATORY-BASED FORAGE QUALITY ASSESSMENT APPROACHES ARE LABOR-INTENSIVE AND TIME-CONSUMING, AND THEREBY GREATLY LIMIT THE GENETIC SELECTION AND FORAGE BREEDING EFFICIENCY. TO ENHANCE FORAGE PHENOTYPING CAPACITY, THE OVERALL GOAL OF THIS PROJECT IS TO ASSESS MAIZE SILAGE YIELD AND QUALITY TRAITS IN A HIGH-THROUGHPUT MANNER BY MELDING CUTTING-EDGE HYPERSPECTRAL REMOTE SENSING AND MACHINE LEARNING TECHNOLOGIES IN A FIELD SETTING. SPECIFICALLY, THE PROPOSED RESEARCH ADDRESSES TWO KEY PLANT PHENOTYPING CHALLENGES FROM THE DATA SCIENCE PERSPECTIVES BY: (1) DEVELOPING MULTI-TEMPORAL FEATURE FUSION APPROACHES TO FULLY EXPLOIT THE POTENTIAL OF TIME-SERIES HYPERSPECTRAL DATA; (2) DEVELOPING UNSUPERVISED DOMAIN ADAPTATION STRATEGIES TO INCREASE THE MODEL TRANSFERABILITY ACROSS DIFFERENT ENVIRONMENTS TO AVOID CONTINUOUS LABEL EFFORT ASSOCIATED WITH ENVIRONMENTAL CHANGES. THE MAIZE SILAGE MIX IS MORE COMPLEX THAN OTHER FORAGE SPECIES AS IT INCLUDES GRAIN AND STOVER, THEREFORE METHODS DEVELOPED FOR THIS PLANT STRUCTURE CAN POTENTIALLY BE APPLIED TO OTHER FORAGE COMMODITIES WITH LESS COMPLEX PLANT STRUCTURES FOR BIOCHEMICAL COMPOSITION ASSESSMENT. FURTHER, SINCE THE HIGH DIMENSIONALITY OF THE INPUT DATA AND THE LIMITED NUMBER OF LABELED SAMPLES ARE TWO KEY CHALLENGES THAT AFFECT THE PERFORMANCE OF MACHINE LEARNING MODELS, FUTURE PROJECTS WILL LEVERAGE THESE DEVELOPED AI MODELS IN OTHER AGRICULTURAL APPLICATIONS WHERE HYPERSPECTRAL DATA ARE OFTEN EMPLOYED (E.G., DISEASE DETECTION, STRESS ASSESSMENT, ETC).