AN AI-ENHANCED MULTISPECTRAL VISION-BASED IN-FIELD APPLE SORTING SYSTEM

Theoverall objectiveof this project is to develop an innovative, artificial intelligence-enhanced multispectral vision-based in-field sorting system for apples, with the capabilities of sorting harvested fruit for size, color, and surface defects into three grades (i.e., "Fresh", "Processing", and "Cull"). Our specific objectives are to: 1) develop a multispectral imaging system integrated with a screw conveyor for acquiring images from traveling apples, 2)develop deep learning models for grading apples according to quality conditions, with emphasis on defects, 3) develop a new sorter integrated with the proposed multispectral vision system to sort apples into three grades, 4)develop user-friendly, open-sourced software programs for system operation and real time visualization, and 5)perform system integration to build an in-field apple sorting machine prototype and conduct field tests and demonstration in commercial orchards.