DeepWeeds: a multiclass weed species image dataset for deep learning
Olsen, Alex, Konovalov, Dmitry A., Philippa, Bronson, Ridd, Peter, Wood, Jake, Johns, Jamie, Banks, Wesley, Girgenti, Benjamin, Kenny, Owen, Whinney, James, Calvert, Brendan, Rahimi Azghadi, Mostafa, and White, Ronald D. (2019) DeepWeeds: a multiclass weed species image dataset for deep learning. Scientific Reports, 9. 2058.
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Abstract
Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.
| Item ID: | 60792 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2045-2322 |
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| Copyright Information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Additional Information: | A version of this publication was included as Chapter 3-4 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. |
| Funders: | Department of Agriculture and Water Resources (DAW) |
| Projects and Grants: | DAW Control Tools and Technologies for Established Pest Animals and Weeds Programme Grant No. 4-53KULEI |
| Date Deposited: | 04 Nov 2019 01:34 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 100% |
| SEO Codes: | 96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960805 Flora, Fauna and Biodiversity at Regional or Larger Scales @ 100% |
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