In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this area relies on having prior knowledge of the weed species present in the field. This assumption cannot always hold true for every field, and thus limits the use of weed classification systems based on this assumption. In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment. We present a three stage pipeline for the implementation of our weed classification system consisting of initial field surveillance, offline processing and selective labelling, and automated precision weeding. The key characteristic of our approach is the combination of plant clustering and selective labelling which is what enables our system to operate without piror weed species knowledge. Testing using field data we are able to label 12.3 times fewer images than traditional full labelling whilst reducing classification accuracy by only 14%.
Hall et al. REF propose a rapidly deployable weed classification system for precision weed management.
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
D. Hall, F. Dayoub, T. Perez, and C. McCool,"A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management,"Computers and Electronics in Agriculture, Vol. 148, pp. 107-120, May 2018
Computer Science Engineering