TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING
Mahmoud BAKR
mah.bakr.2005@gmail.comClimate Change Information Center and Expert Systems, Agricultural Research Center, (Egypt)
Sayed ABDEL-GABER
Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, (Egypt)
Mona NASR
Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, (Egypt)
Maryam HAZMAN
Climate Change Information Center and Expert Systems, Agricultural Research Center (Egypt)
Abstract
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Keywords:
Leaf Disease Detection, Convolutional Neural Network, Deep Learning, Transfer LearningReferences
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Authors
Mahmoud BAKRmah.bakr.2005@gmail.com
Climate Change Information Center and Expert Systems, Agricultural Research Center, Egypt
Authors
Sayed ABDEL-GABERFaculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Authors
Mona NASRFaculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Authors
Maryam HAZMANClimate Change Information Center and Expert Systems, Agricultural Research Center Egypt
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