Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms

By Henry Williams, Mark H. Jones, Mahla Nejati, Matthew Seabright, Jamie Bell, Nicky Penhall, J. Barnett, M. Duke, Alistair J. Scarfe, H. Ahn and 2 more. Biosystems Engineering, 2019

300 citations recorded by the source corpus.

As labour requirements in horticultural become more challenging, automated solutions are becoming an effective approach to maintain productivity and quality. This paper presents the design and performance evaluation of a novel multi-arm kiwifruit harvesting robot designed to operate autonomously in pergola style orchards. The harvester consists of four robotic arms that have been designed specifically for kiwifruit harvesting, each with a novel end-effector developed to enable safe harvesting of the kiwifruit. The vision system leverages recent advances in deep neural networks and stereo matching for reliably detecting and locating kiwifruit in real-world lighting conditions. Furthermore, a novel dynamic fruit scheduling system is presented that has been developed to coordinate the four arms throughout the harvesting process. The performance of the harvester has been measured through a comprehensive and realistic field-trial in a commercial orchard environment. The results show that the presented harvester is capable of successfully harvesting 51.0% of the total number of kiwifruit within the orchard with an average cycle time of 5.5s/fruit.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/daceaf9d870e025a1d694c107eb94f8398ab75a0

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Citations in the corpus

A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, methodology): “An example of automatic fruit detection is the work presented by Williams et al. [84], where they show a multi-arm kiwi pickup robot, presenting the design and evaluation of the robot’s performance.”
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review (2020, background): “The background and illumination of the crops vary continuously (Bulanon et al., 2010; Zhao et al., 2011; Qingchun et al., 2012; Hemming et al., 2014; Silwal et al., 2016; Liu et al., 2019; Williams H. et al., 2019; Zhuang et al., 2019).”