{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepweeds-a-multiclass-weed-species-image","title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","arxiv_id":"1810.05726","date":"2018-10-09","proceeding":null,"authors":["Alex Olsen","Dmitry A. Konovalov","Bronson Philippa","Peter Ridd","Jake C. Wood","Jamie Johns","Wesley Banks","Benjamin Girgenti","Owen Kenny","James Whinney","Brendan Calvert","Mostafa Rahimi Azghadi","Ronald D. White"],"abstract":"Robotic weed control has seen increased research of late with its potential\nfor boosting productivity in agriculture. Majority of works focus on developing\nrobotics for croplands, ignoring the weed management problems facing rangeland\nstock farmers. Perhaps the greatest obstacle to widespread uptake of robotic\nweed control is the robust classification of weed species in their natural\nenvironment. The unparalleled successes of deep learning make it an ideal\ncandidate for recognising various weed species in the complex rangeland\nenvironment. This work contributes the first large, public, multiclass image\ndataset of weed species from the Australian rangelands; allowing for the\ndevelopment of robust classification methods to make robotic weed control\nviable. The DeepWeeds dataset consists of 17,509 labelled images of eight\nnationally significant weed species native to eight locations across northern\nAustralia. This paper presents a baseline for classification performance on the\ndataset using the benchmark deep learning models, Inception-v3 and ResNet-50.\nThese models achieved an average classification accuracy of 95.1% and 95.7%,\nrespectively. We also demonstrate real time performance of the ResNet-50\narchitecture, with an average inference time of 53.4 ms per image. These strong\nresults bode well for future field implementation of robotic weed control\nmethods in the Australian rangelands.","url_abs":"http://arxiv.org/abs/1810.05726v3","url_pdf":"http://arxiv.org/pdf/1810.05726v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepweeds-a-multiclass-weed-species-image","repo_url":"https://github.com/AlexOlsen/DeepWeeds","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"management","task_name":"Management"},{"task_slug":"robust-classification","task_name":"Robust classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"inception-v3","method_name":"Inception-v3"},{"method_slug":"inception-v3-module","method_name":"Inception-v3 Module"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"deepweeds","name":"DeepWeeds","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.05726","atlas_url":"https://app.syntology.ai/?focus=1810.05726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05726"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/AlexOlsen/DeepWeeds","reach":null}],"summary":{"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"a5f1ce209ba31369","entry":"crop","repo":"AlexOlsen/DeepWeeds","repo_kind":"official","path":"deepweeds.py","file_url":"https://github.com/AlexOlsen/DeepWeeds/blob/HEAD/deepweeds.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a5f1ce209ba31369"}},{"code_sha256_prefix":"135b3dc835ffe6ad","entry":"get_confirm_token","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"135b3dc835ffe6ad"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}