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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. Sensors 2020, 20, 93 2 of 15 the crop peduncle based on the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm [10].The rest of this paper is organized as follows: In Section 2, we introduce related work. In Section 3, we describe the design of the gripper and provide a mechanical analysis. In Section 4, we present the approach of cutting-point detection. In Section 5, we describe an experimental robotic system to demonstrate the harvesting process for artificial and real fruits and vegetables. Conclusions and future work are given in Section 6. Related WorkDuring the last few decades, many systems have been developed for the autonomous harvesting of soft crops, ranging from cucumber [11] and tomato-harvesting robots [12] to sweet-pepper [2,13] and strawberry-picking apparatus [14,15]. The work in [16] demonstrated a sweet-pepper-harvesting robot that achieved a success rate of 6% in unstructured environments. This work highlighted the difficulty and complexity of the harvesting problem. The key research challenges include manipulation tools along with the harvesting process, and perception of target fruits and vegetables with the cutting points.

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