Viewpoint Planning for Automated Fruit Harvesting Using Deep Learning | IEEE Conference Publication | IEEE Xplore

Viewpoint Planning for Automated Fruit Harvesting Using Deep Learning


Abstract:

This study presents a viewpoint planning for harvesting robots to improve the detection results of fruits. With viewpoint planning, robots can consider active sensing tec...Show More

Abstract:

This study presents a viewpoint planning for harvesting robots to improve the detection results of fruits. With viewpoint planning, robots can consider active sensing techniques instead of considering just one usual viewpoint. The planner takes the current scene as input and outputs the best viewpoint out of pre-defined viewpoints; move left, move right, or stay at the current position. We formulate the viewpoint planning problem as a classification problem and implement it using a deep neural network. We extract local fruit regions with the neighboring regions surrounding the fruit from each current scene using a fruit detector. After applying fruit-wise classification, we use the labels assigned by the classifier in the viewpoint planner to select the best viewpoint. Our system classifies fruits up to 82.9 and 81 percent accuracy on unseen test data for computer graphics and real farm datasets, respectively. Overall, we conclude that deep learning is a promising field of research for advancing the latest technology in harvesting robots.
Date of Conference: 11-14 January 2021
Date Added to IEEE Xplore: 24 March 2021
ISBN Information:

ISSN Information:

Conference Location: Iwaki, Fukushima, Japan

Contact IEEE to Subscribe

References

References is not available for this document.