An Advanced Cartesian Robotic System for Precision Apple Crop Load Management

Crop load management is the single most important yet difficult management strategy that determines the annual profitability of apple orchards. Pollination and thinning are the two aspects that affect greatly on effective crop load management. Traditional apple flower pollination relies heavily on renting hives of honeybees, which are declining rapidly. Environmental conditions also interfere with the natural pollination process thus causing huge uncertainty in achieving optimal pollination. Later after the pollination process, to apply appropriate amount of thinning remains a challenge: If thinning is inadequate and too many fruits remain on the tree, fruit size will be small, fruit quality will be poor and flower bud initiation for the following year's crop may be either reduced or eliminated. Over-thinning also carries economic perils since yield and crop value at the year of application will be reduced. Thus, in this study, we propose to develop a robotic apple crop load management system to achieve high yield and quality of fruit crops resulting in a substantial economic benefit to the tree fruit industry. The proposed robotic system consists of two major components; i) a well-developed machine vision system that can identify the location of apple flower clusters and king flowers, generate flower density map, and communicate with robotic manipulator automatically; and ii) a cartesian robotic system to reach target position. It is expected that our prototype and field validation can provide guideline information for developing a robotic crop load management system for apple growers.