Beyond mAP: Towards practical object detection for weed spraying in precision agriculture

Salazar-Gomez, Adrian, Darbyshire, Madeleine, Gao, Junfeng , Sklar, Elizabeth and Parsons, Simon (2022) Beyond mAP: Towards practical object detection for weed spraying in precision agriculture. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, 23-27 Oct 2022, Kyoto, Japan.

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Beyond mAP: Towards practical object detection for weed spraying in precision agriculture
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Abstract

The evolution of smaller and more powerful GPUs over the last 2 decades has vastly increased the opportunity to apply robust deep learning-based machine vision approaches to real-time use cases in practical environments. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically and environmentally. This paper assesses the feasibility of precision spraying weeds via a comprehensive evaluation of weed detection accuracy and speed using two separate datasets, two types of GPU, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is used to determine whether the weed detection accuracy achieved could result in a sufficiently high weed hit rate combined with a significant reduction in herbicide usage. The paper introduces two metrics to capture these aspects of the real-world deployment of precision weeding and demonstrates their utility through experimental results.

Keywords:precision agriculture, automated weeding, computer vision, object detection
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
D Veterinary Sciences, Agriculture and related subjects > D470 Agricultural Technology
D Veterinary Sciences, Agriculture and related subjects > D410 Arable and Fruit Farming
Divisions:College of Science > School of Computer Science
ID Code:51680
Deposited On:13 Sep 2022 09:23

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