Tomato disease detection model based on densenet and transfer learning

• Autorzy: Bakr Mahmoud , Abdel-Gaber Sayed , Nasr Mona , Hazman Maryam

Identyfikatory

DOI

10.35784/acs-2022-13

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

leaf disease detection; convolutional neural network; deep learning; transfer learning

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 2
• Strony: 56--70
• Opis fizyczny: Bibliogr. 21 poz., fig., tab.

Twórcy

autor

Bakr Mahmoud

• Climate Change Information Center and Expert Systems, Agricultural Research Center, Egypt

autor

Abdel-Gaber Sayed

• Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt

autor

Nasr Mona

• Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt

autor

Hazman Maryam

• Climate Change Information Center and Expert Systems, Agricultural Research Center, Egypt

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).