Author:
Goncharov Pavel,Uzhinskiy Alexander,Ososkov Gennady,Nechaevskiy Andrey,Zudikhina Julia
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 %.
Reference10 articles.
1. Using Deep Learning for Image-Based Plant Disease Detection
2. Brahimi M.,
Arsenovic M.,
Laraba S.,
Sladojevic S.,
Boukhalfa K.,
Moussaoui A., Deep learning for plant diseases: detection and saliency map visualisation, in Human and Machine Learning (eds Zhou J. and Chen F., Cham: Springer International Publishing, 2018) 93–117
3. Deep learning models for plant disease detection and diagnosis
4. A comparative study of fine-tuning deep learning models for plant disease identification
5. Goncharov P.,
Ososkov G.,
Nechaevskiy A.,
Uzhinskiy A.,
Nestsiarenia I,
Disease Detection on the Plant Leaves by Deep Learning, International Conference on Neuroinformatics 151–159
(2018)
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