DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning

By A. Olsen, Dmitry A. Konovalov, B. Philippa, P. Ridd, Jake C. Wood, Jamie Johns, Wesley Banks, Benjamin Girgenti, Owen Kenny, J. Whinney and 3 more. Scientific Reports, 2018

493 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Insect Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/09d2e4741c85670c5db937b664b007b1e354bfc6

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In Plant Science

Citations in the corpus

A Survey of Deep Learning Techniques for Weed Detection from Images (2021, methodology): “While acquiring data for the “DeepWeeds” dataset, the researchers added a “Fujinon CF25HA-1” lens with their “FLIR Blackfly 23S6C” camera and mounted the camera on a weed control robot (“AutoWeed”).”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, background): “Oslen et al. [86] explored robust deep learning models Inception-v3 and ResNet-50 for weed species identiﬁcation and classiﬁcation from a dataset of images collected in Australian rangeland.”
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “But other efficient DL models (Faster RCNN or RFCN) should still be tested to further investigate the effectiveness of SSD for that task (Onishi et al., 2019).”