Abstract
Reliable and robust systems to detect and harvest fruits and vegetables in unstructured environments are crucial for harvesting robots. In this paper, we propose an autonomous system that harvests most types of crops with peduncles. A geometric approach is first applied to obtain the cutting points of the peduncle based on the fruit bounding box, for which we have adapted the model of the state-of-the-art object detector named Mask Region-based Convolutional Neural Network (Mask R-CNN). We designed a novel gripper that simultaneously clamps and cuts the peduncles of crops without contacting the flesh. We have conducted experiments with a robotic manipulator to evaluate the effectiveness of the proposed harvesting system in being able to efficiently harvest most crops in real laboratory environments.
Highlights
Manual harvesting of fruits and vegetables is a laborious, slow, and time-consuming task in food production [1]
Cutting at the peduncle leads to higher success rates for crop detachment, and detachment at the peduncle reduces the risk of damaging the flesh or other stems of the plant and maximizes the storage life
The results show that the detection success rates for plastic and real crops are 93% (25/27) and 87% (27/31), Sensors
Summary
Manual harvesting of fruits and vegetables is a laborious, slow, and time-consuming task in food production [1]. Automatic harvesting has many benefits over manual harvesting, e.g., managing the crops in a short period of time, reduced labor involvement, higher quality, and better control over environmental effects. These potential benefits have inspired the wide use of agricultural robots to harvest horticultural crops (the term crops is used indistinctly for fruits and vegetables throughout the paper, unless otherwise indicated) over the past two decades [2]. An autonomous harvesting robot usually has three subsystems: a vision system for detecting crops, an arm for motion delivery, and an end-effector for detaching the crop from its plant without damaging the crop. Existing manipulation tools that realize both grasping and cutting functions usually require a method of additional detachment [8,9], which increases the cost of the entire system
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