Context

The DeepWeedsX dataset consists of 17,508 unique 256x256 colour images in 9 classes. There are 15,007 training images and 2,501 test images. These images were collected in situ from eight rangeland environments across northern Australia.

Liaison with land care groups and property owners across northern Australia led to the selection of eight target weed species for the the collection of a large weed species image dataset; Chinee Apple (Ziziphus mauritiana), Lantana, Parkinsonia (Parkinsonia aculeata), Parthenium (Parthenium hysterophorus), Prickly Acacia (Vachellianilotica), Rubber vine (Cryptostegia grandiflora), Siam weed (Chromolaena odorata) and Snakeweed (Stachytarphetaspp).

DeepWeedsX is a subset of the DeepWeeds dataset, which was originally collected by Alex Olsen, and has previously been made openly accessible. We present a labeled variant with clearly defined training and test datasets. A validation dataset may be constructed for parameter optimization using a subset of the labeled training dataset.

Content

All class label files consist of Comma Seperated Values (CSVs) detailing the label and species, for example: 20161207-111327-0.jpg, 0 denotes that 20161207-111327-0.jpg belongs to class 0 (Chinee Apple).

Class and species labels are as follows:

0- Chinee Apple 1- Lantana 2- Parkinsonia 3- Parthenium 4- Prickly Acacia 5- Rubber Vine 6- Siam Weed 7- Snake Weed 8- Other.

All images are compressed in a single ZIP archive, and are labelled as per the class file labels.

Citation

To cite the DeepWeedsX dataset, kindly use the following BibTex entry:

@ARTICLE{8693488, author={C. {Lammie} and A. {Olsen} and T. {Carrick} and M. R. {Azghadi}}, journal={IEEE Access}, title={Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge}, year={2019}, volume={}, number={}, pages={1-1}, keywords={Machine Learning (ML);Deep Neural Networks (DNNs);Convolutional Neural Networks (CNNs);Binarized Neural Networks (BNNs);Internet of Things (IoT);Field Programmable Gate Arrays (FPGAs);High-level Synthesis (HLS);Weed Classification}, doi={10.1109/ACCESS.2019.2911709}, ISSN={2169-3536}, month={},}

Acknowledgements

All original data collection was funded by the Australian Government Department of Agriculture and Water Resources Control Tools and Technologies for Established Pest Animals and Weeds Programme (Grant No. 4-53KULEI).