Study of Canopy-Machine Interaction in Mass Mechanical Harvest of Fresh Market Apples

Download or read book Study of Canopy-Machine Interaction in Mass Mechanical Harvest of Fresh Market Apples written by Xin Zhang and published by . This book was released on 2020 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fresh-market apple is one of the high-value agricultural produces in the United States and Washington. These apples are harvested manually worldwide, which requires a large seasonal workforce. Due to uncertain availability and rising cost of labor, the need for mechanical harvesting technologies has become critically important. Shake-and-catch harvesting technology has been studied to address this issue. Major challenges for mechanically harvesting fresh-market fruit include insufficient fruit removal, high fruit damage, and low labor productivity. As a way to address these challenges, this study focused on understanding canopy responses to the harvesting system through employing a supervised machine learning algorithm. Specifically, it aimed at identifying the most relevant canopy parameters influencing the fruit removal during mechanical harvesting. Based on the analysis of apples 'harvested' mechanically and those that remained on the trees after harvesting operation, fruit load, branch diameter, and shoot length/diameter were found to be the canopy parameters highly relevant to the success of mechanical harvesting techniques. Field tests, therefore, revealed that the pruning strategies have a remarkable influence on fruit removal efficiency. It was found that, to maintain a minimum removal efficiency of 85%, the shoot length should be less than 15 cm or S-index (the ratio of shoot diameter to length) should be >0.03.This study also included a comprehensive evaluation for comparing different harvesting systems based on multi-year/cultivar field trials. The results showed that the semi-automated system was more effective (fruit removal efficiency of 90%) compared to the hand-held (87%) and the manually operated hydraulic systems (84%). To further advance the automated machine operation, a machine vision (deep learning-based) system was developed for detecting and localizing tree trunks and branches, which achieved an intersection over union (the ratio of overlapping to total area) of 0.69 in trunk/branch detection. Polynomial curves were then employed for fitting the branches/trunks through the detected segments, which was used in estimating shaking locations on those branches. This research served as a basis for optimizing and advancing shake-and-catch harvesting technologies on fresh-market apple harvesting, which is expected to make a huge, positive impact on the long-term economic sustainability of apple industry.