Abstract
Convolutional Neural Network is a deep learning method that covers several fields, including the agronomy sector. This new method provides fast and very encouraging results for diagnosis disease. As part of this work, we built a model of early diagnosis of the wheat plant in Moroccan region based on the Convolutional Neutral Network. Our goal is to allow rapid recognition of contaminated plants at early stages of appearance in order to provide the necessary treatment and limit propagation damage. Our study begins with a set image collection of wheat plants in the field and in the laboratory. We collect two type of images. Images of healthy plants and others that display three diseases, namely Brown rust, Septoria and Powdery Mildew. A first phase of data processing consisted in segmenting and augmenting these images. The next step is the model architecture choice, the network training and finally the evaluation phase. The validation of our model by test images, allowed us to reach 97% as an accuracy level in the wheat diseases diagnosis.
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Aitelkadi, K., Chtaina, N., Bakouri, S., Belebrik, M. (2021). Early Detection of Wheat Diseases in Morocco by Convolutional Neutral Network. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_3
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DOI: https://doi.org/10.1007/978-3-030-73882-2_3
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