EPJ Web of Conferences 226, 03010 (2020)
https://doi.org/10.1051/epjconf/202022603010

Deep Siamese Networks for Plant Disease Detection

¹ Dubna State University, Universitetskaya 19, 141982, Dubna, Moscow region, Russia
² Joint Institute for Nuclear Research, 6 Joliot-Curie, 141980, Dubna, Moscow region, Russia
³ Ural Federal University, 19 Mira street, 620002, Ekaterinburg, Russia

^★ e-mail: kaliostrogoblin3@gmail.com
^★★ e-mail: auzhinskiy@jinr.ru

Published online: 20 January 2020

Abstract

Crop losses are a major threat to the wellbeing of rural families, to the economy and governments, and to food security worldwide. The goal of our research is to develop a multi-functional platform to help the farming community to tilt against plant diseases. In our previous works, we reported about the creation of a special database of healthy and diseased plants’ leaves consisting of five sets of grapes images and proposed a special classification model based on a deep siamese network followed by k-nearest neighbors (KNN) classifier. Then we extended our database to five sets of images for grape, corn, and wheat – 611 images in total. Since after this extension the classification accuracy decreased to 86 %, we propose in this paper a novel architecture with a deep siamese network as feature extractor and a single-layer perceptron as a classifier that results in a significant gain of accuracy, up to 96 %.