Development of a sweet pepper harvesting robot

By Boaz Arad, J. Balendonck, R. Barth, Ohad Ben-Shahar, Yael Edan, Thomas Hellström, Jochen Hemming, P. Kurtser, Ola Ringdahl, Toon Tielen and 1 more. J. Field Robotics, 2020

386 citations recorded by the source corpus.

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB‐D camera, high‐end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end‐user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4‐week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.

Subfields: Mechanical Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/f556fd9d56851597a73038256af83147760a5c1a

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Citations in the corpus

System Design and Control of an Apple Harvesting Robot (2020, neutral): “So far, different systems have been designed for semi-automation or total automation of agriculture harvesting [3–7].”
Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar (2020, background): “Segmentation allows for more accurate object localisation and picking [4].”
Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead (2021, positive): “The Harvey platform opted for the use of Deep Convolutional Neural Network (DCNN), while the SWEEPER robot used the techniques of deep learning, shape and color-based detection algorithm and Hough Transform (HT) [84–86].”
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review (2020, background): “%) with a cycle time of 33 s per fruit (values ranging between 1 and 227 s) (Arad et al., 2020).”