Abstract
Vietnamese agriculture aims to develop sustainable intensification with the help of modern technology. Therefore, it is necessary to provide an intelligent system-based artificial intelligence to help farmers to analyze and diagnose the plant leaves disease to increase the plant health and productivity. Various systems have been proposed to detect and classify plant leaf diseases, such as convolutional neural networks (CNN), support vector machines (SVM), decision trees, KNN, and random forests. However, the performance of these existing systems is still not satisfactory for real applications. Therefore, we proposed a novel approach to accurately detect and classify plant leaf disease by using the EfficientNet and Residual block. The proposed system obtained better results than existing methods, such as CNN, SVM, KNN, random forest, and decision tree. The proposed system was able to detect and classify the blueberry healthy with a detection rate of 97.24%, the blueberry disease with the detection rate of 94.24%, the apple black rot with a detection rate of 95.14%, the tomato bacterial spot with a detection rate of 98.24%, and the potato late blight with the detection rate of 98.87%.
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Acknowledgements
This project was funded by the Ministry of Education and Training, Vietnam, with grant number: B2021-TCT-10. The authors are grateful to the Department of Software Engineering, School of Computing and Information Technology, Eastern International University (EIU), Vietnam.
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Nguyen, V.D., Ngo, N.P., Le, Q.N., Debnath, N.C. (2023). Robust Plant Leaves Diseases Classification Using EfficientNet and Residual Block. In: So-In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 324. Springer, Singapore. https://doi.org/10.1007/978-981-19-7447-2_12
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DOI: https://doi.org/10.1007/978-981-19-7447-2_12
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