RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK

Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses  Setiadi; Arnold Adimabua  Ojugo

doi:10.52436/1.jutif.2023.4.6.1529

Ahmad Rofiqul Muslikh Faculty of Information Technology, Universitas Merdeka, Malang, East Java, Indonesia
De Rosal Ignatius Moses Setiadi Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Central Java, Indonesia
Arnold Adimabua Ojugo Department of Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

DOI: https://doi.org/10.52436/1.jutif.2023.4.6.1529

Keywords: Convolutional Neural Network, Image recognition, Paddy disease identification, Transfer Learning, Xception pre-trained model

Abstract

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.

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