EPJ Web Conf.
Volume 226, 2020Mathematical Modeling and Computational Physics 2019 (MMCP 2019)
|Number of page(s)||4|
|Section||Mathematical and Computational Support of the Experiments, Computing Tools, and Software Services|
|Published online||20 January 2020|
Deep Siamese Networks for Plant Disease Detection
Dubna State University,
Dubna, Moscow region,
2 Joint Institute for Nuclear Research, 6 Joliot-Curie, 141980, Dubna, Moscow region, Russia
3 Ural Federal University, 19 Mira street, 620002, Ekaterinburg, Russia
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
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