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In this work we demonstrate a rapidly deployable weed classification systemthat uses visual data to enable autonomous precision weeding without makingprior assumptions about which weed species are present in a given field.Previous work in this area relies on having prior knowledge of the weed speciespresent in the field. This assumption cannot always hold true for every field,and thus limits the use of weed classification systems based on thisassumption. In this work, we obviate this assumption and introduce a rapidlydeployable approach able to operate on any field without any weed speciesassumptions prior to deployment. We present a three stage pipeline for theimplementation of our weed classification system consisting of initial fieldsurveillance, offline processing and selective labelling, and automatedprecision weeding. The key characteristic of our approach is the combination ofplant clustering and selective labelling which is what enables our system tooperate without prior weed species knowledge. Testing using field data we areable to label 12.3 times fewer images than traditional full labelling whilstreducing classification accuracy by only 14%.

A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management