Abstract
Plant diseases are one of the main factors that can cause significant crop damages and yield losses. Deep learning has recently attracted a lot of attention to use for plant disease identification. The recent studies used deep learning techniques to diagnose plant diseases in an attempt to identify plant diseases. This paper evaluates the performance of different deep learning methods, including YOLO, RetinaNet, Faster RCNN, and Mask RCNN, for plant disease-identification problem. The real dataset of 5,180 leaves and 42 types of leaf samples (disease or not) from different crops were used for evaluation.
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Nguyen, V.T., Duong, T.Q., Le, T.D., Nguyen, A.T.D. (2020). Deep Learning-Based Methods for Plant Disease. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_12
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