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PlanTech: Early Detection of Plant Disease Based on HWSN Using Deep Learning

Soufiane Kaissari(1*), Amina El Attaoui(2), Achraf Benba(3), Abdelilah Jilbab(4), Abdennaser Bourouhou(5), Abdellah Kaissari(6)

(1) University Mohammed V in Rabat, Morocco
(2) University Mohammed V in Rabat, Morocco
(3) University Mohammed V in Rabat, Morocco
(4) University Mohammed V in Rabat, Morocco
(5) University Mohammed V in Rabat, Morocco
(6) Ecole Centrale de Lyon, France
(*) Corresponding author



Plant diseases are a major threat to food security. Thus, the identification of these diseases is crucial to alleviate the problem. Deep learning combined with image processing has proven to be efficient in order to identify such diseases accurately, automatically and quickly. This article presents a general architecture for plant disease prediction based on the Hybrid Wireless Sensor Network, the Internet of Things, and Deep Learning algorithms. In this work, the focus is on learning models for plant disease detection to be implemented afterward on the structure for actual trials. Thus, several deep learning architectures have been tested, i.e. ResNet50, 101, and 152, AlexNet, VGG, SqueezeNet, DenseNet, and Inception. In addition to that, a new parallel architecture has been developed and evaluated by combining two deep learning architectures. Several optimizers such as SGD and ADAM have been leveraged in order to improve the performances. The public published database PlantVillage is used for model learning, which contains 54.306 images of agricultural plant leaves. As a result, the best validation accuracy score of 98,09% is achieved using the presented para model with ADAM optimizer running with the 80/20 splitting data on the 14th epoch.
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Deep Learning; Convolution Neural Network; Computer Vision; Plant Disease; Hybrid Wireless Sensor Network

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