Analysis of a dynamically adjustable multi-level soft fruit catching system design for mechanical harvesting - Kaiming Fu; Yuankai Zhu; Ethan H. Frehner; Kyle T. Rizzo; Stavros G. Vougioukas; Brian N. Bailey (Smart Agricultural Technology, 2025)
Harvesting soft, fresh-market fruits has traditionally been a labor-intensive process that requires hand-picking to minimize damage that compromises marketability during harvesting. Robotic harvesters have been proposed to automate this process, but they still generally lack acceptable speed, accuracy, or damage prevention. Mass shake-catch harvesters can achieve acceptable speed, but they lead to excessive fruit damage due to impacts with branches, other fruit, and the harvester itself. In our previous work, we introduced a computer-aided design (CAD) framework to design and evaluate the potential for multi-level fruit catching and retrieval (MFCR) harvesters that minimize fruit damage during shake-catch harvesting by using multiple layers of catch frames to reduce fruit falling distance. In this work, the tool was enhanced to improve computational efficiency and enable multi-parameter design optimization. The ultimate goal of this work was to use the tool to assess whether a dynamically adjustable harvester design, guided by real-time computer vision, may be worthwhile in terms of the relative increase in harvester efficiency relative to the increased difficulty and cost to design and manufacture such a harvester. Results suggested that a dynamic MFCR harvester that could dynamically adjust the vertical positions of catching arm layers based on the detected fruit distribution could increase the marketable fruit collection rate by at least 10%, and reduce tree-to-tree standard deviation by 2-5×, relative to an optimized fixed design.