DenseNet Based Model for Plant Diseases Diagnosis

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  •   Mahmoud Bakr

  •   Sayed Abdel-Gaber

  •   Mona Nasr

  •   Maryam Hazman

Abstract

The biggest threat to the safety of food is plant diseases. They have the ability to dramatically lower the quantity and quality of agricultural products. Recognizing plant diseases is the biggest issue in the agricultural industries. Convolutional Neural Networks (CNN) are effective in solving image classification problems in computer vision. Numerous deep learning architectures have been used to diagnose plant diseases. This study presents a transfer learning-based model for identifying diseases in plant leaves. In this paper, a CNN classifier based on transfer learning model called DenseNet201 are proposed. An analysis of four deep learning models (VGG16, Inception V3, ResNet152V2, and DenseNet201) done to see which one can detect plant diseases with the greatest degree of accuracy. Web based application developed for plant disease diagnosing from defected leaf image and the proposed model which identify the disease and give the recommended treatment. The used images dataset contains 28310 leaves photos of 3 crops, tomato, potato and pepper divided into 15 different classes, 9 disorders and one healthy class for tomato, 2 disorders and one healthy class for potato and 1 disorder and one healthy for pepper. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.44% and validation accuracy of 98.70%.


Keywords: Convolutional Neural Network, Deep Learning, Leaf Disease Detection, Transfer Learning.

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How to Cite
[1]
Bakr, M., Abdel-Gaber, S., Nasr, M. and Hazman, M. 2022. DenseNet Based Model for Plant Diseases Diagnosis. European Journal of Electrical Engineering and Computer Science. 6, 5 (Sep. 2022), 1–9. DOI:https://doi.org/10.24018/ejece.2022.6.5.458.