Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms
Williams, H., Jones, M. H., Nejati, M., Seabright, M., Bell, J., Penhall, N. D., … MacDonald, B. A. (2019). Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 181, 140–156. https://doi.org/10.1016/j.biosystemseng.2019.03.007
Permanent Research Commons link: https://hdl.handle.net/10289/13355
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.
This is an author's pre-print of an article published in Biosystems Engineering. © 2019 IAgrE.