Autoweed: detecting Harrisia cactus in the Goondiwindi region for selective spot-spraying

Calvert, Brendan, Olsen, Alex, Philippa, Bronson, and Rahimi Azghadi, Mostafa (2019) Autoweed: detecting Harrisia cactus in the Goondiwindi region for selective spot-spraying. In: https://www.wsq.org.au/downloads/2019-pest-animal-and-weed-symposium-proceedings/. pp. 52-57. From: PAWS 2019: 1st Queensland Pest Animal and Weed Symposium, 20-23 May 2019, Gold Coast, QLD, Australia.

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Abstract

Innovations in the field of robotics have sought to combine technology with farming to provide efficient solutions for the ever-present problem of invasive weeds. Research has focused on the development of robotic solutions for use in croplands, while rangeland weed management techniques have remained largely unchanged. In response, we developed a prototype robotic spot-sprayer for pass over foliar spraying in Australian rangelands that we call AutoWeed. In partnership with North West Local Land Services, we take aim at Harrisia cactus (Eriocereus martini)in the Goondiwindi region to determine its suitability as a potential target. We collected an image dataset of Harrisia cactus comprising over 2,000 images of target and non-target plant life in the Goondiwindi region. Using state-of-the-art convolutional neural networks (CNNs) to detect the Harrisia cactus, we achieved an average classification accuracy of 98.14%, which is an encouraging result. To understand the model's learning, class activation maps were utilised to generate heatmaps that identify regions of interest. It was discovered that the CNN models could correctly discriminate between Harrisia cactus and its surroundings despite the highly variable nature of the environment. These results prove the target suitability of Harrisia cactus for future field trials of the AutoWeed spot-sprayer robot.

Item ID: 62461
Item Type: Conference Item (Research - E1)
Keywords: deep learning, convolutional neural networks, AutoWeed, class activation maps, transfer learning, weed detection
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A version of this publication was included as Chapter 4-5 of the following PhD thesis: Olsen, Alex (2020) Improving the accuracy of weed species detection for robotic weed control in complex real-time environments. PhD thesis, James Cook University, which is available Open Access in ResearchOnline@JCU. Please see the Related URLs for access.

Date Deposited: 05 Apr 2020 22:40
FoR Codes: 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling @ 40%
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300210 Sustainable agricultural development @ 30%
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 30%
SEO Codes: 86 MANUFACTURING > 8614 Machinery and Equipment > 861401 Agricultural Machinery and Equipment @ 100%
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