Potato blackleg is a tuber-borne bacterial disease caused by species within the genera Dickeya and Pectobacterium that can cause decay of plant tissue and wilting through the action of cell wall degrading enzymes released by the pathogen. In case of serious infections, tubers may rot before emergence. Management is largely based on the use of pathogen-free seed potato tubers. For this, ﬁelds are visually monitored both for certiﬁcation and also to take out diseased plants to avoid spread to neighboring plants. Imaging potentially oﬀers a quick and non-destructive way to inspect the health of potato plants in a ﬁeld. Early detection of blackleg diseased plants with modern vision techniques can signiﬁcantly reduce costs. In this paper, we studied the use of deep learning for detecting blackleg diseased potato plants. Two deep convolutional neural networks were trained on RGB images with healthy and diseased plants. One of these networks (ResNet18) was experimentally found to produce a precision of 95 % and recall of 91 % for the disease class. These results show that convolutional neural networks can be used to detect blackleg diseased potato plants.
|Number of pages||6|
|Publication status||Published - 2019|
|Event||6th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, AgriControl 2019 - Sydney, Australia|
Duration: 4 Dec 2019 → 6 Dec 2019
|Conference||6th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, AgriControl 2019|
|Period||4/12/19 → 6/12/19|
Afonso, M. V., Blok, P. M., Polder, G., van der Wolf, J. M., & Kamp, J. A. L. M. (2019). Blackleg Detection in Potato Plants using Convolutional Neural Networks. Paper presented at 6th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, AgriControl 2019, Sydney, Australia.