Location: Sugarbeet and Bean Research
Project Number: 5050-43640-003-000-D
Project Type: In-House Appropriated
Start Date: May 19, 2020
End Date: May 18, 2025
1. Enable a new, efficient, and cost-effective robotic technology, coupled with automated infield sorting and quality tracking technologies, for commercial harvesting of apples. 2. Develop a new imaging technology, based on structured illumination integrated with artificial intelligence and advanced data analytics, with substantially improved capabilities for commercial quality inspection of fruits and vegetables.
Development of enabling technologies for automated fruit harvesting and nondestructive quality inspection during postharvest handling can provide an effective solution to the labor availability and cost issues, and enhance production efficiency, product quality, and thus profitability and sustainability for the specialty crop industries. In recent years, much research has been focused on fruit robotic harvesting, but the progress has been slow and unsatisfactory in meeting industry needs, mainly due to the several key technical hurdles encountered in robotic perception (identifying and localizing fruit), manipulation (reaching out for and picking fruit), and systems integration and coordination. While machine vision technology is widely used for postharvest quality inspection of horticultural products, it still is short of meeting industry expectations in detection of quality-degrading defects and symptoms. This research is therefore aimed at developing a new, cost effective robotic technology for automated harvesting of apples and a new generation imaging technology with substantially enhanced capabilities for quality inspection of fruits and vegetables (e.g., pickling cucumber and tomato) during postharvest handling. Innovative concepts and designs, coupled with artificial intelligence, will be used in the development of the new robotic harvesting system for fruit imaging, detection, localization, and picking. The new robotic system will be integrated with the recently developed apple harvest and infield sorting machine, to enable automated harvesting, sorting, grading and tracking of apples in the orchard. Moreover, a new imaging system, using our newly developed technique on improved reflectance for imaging structures, will be assembled to enable rapid, real-time inspection of harvested horticultural products for quality-degrading defects caused by bruising, physiological disorders, and disease infection. The new knowledge and technologies generated from the research will enable growers and packers/processors to achieve significant labor and cost savings in harvesting, enhance product marketability, and reduce postharvest product loss.