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
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 %.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.