https://doi.org/10.1140/epjs/s11734-024-01450-6
Regular Article
Deep learning model for plant disease detection based on visual analysis of leaf infestation area
1
State University of Management, 99 Ryazansky Av., 109542, Moscow, Russia
2
Financial University under the Government of the Russian Federation, 49/2 Leningradsky Ave., 125167, Moscow, Russia
a
dv_serdechnyj@guu.ru
b
korchaginser@gmail.com
Received:
29
October
2024
Accepted:
12
December
2024
Published online:
14
January
2025
The article considers modern methods based on deep learning for solving the problem of plant disease recognition. A comparative analysis of some existing methods is carried out. A modified neural network model is created that allows to surpass existing methods in recognition accuracy and memory costs. Using the VGG16 architecture, a modified convolutional model is developed using Keras. The dataset used were images of plants—tomato leaves, both healthy and diseased. A computational experiment was carried out in comparison with such architectures as VGG16, ResNet-50, and EfficientNet-85. The proposed model allows to detect plant diseases with the best results in computations and accuracy.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.