From a Proposed CNN Model to a Real-World Application in Rice Disease Classification
Hoang Long Nguyen, Thi Ha Tran, Hong Ha Le Thi, Dinh Cong Nguyen
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 177–182 (2022)
Abstract. A compact and precise application of rice disease classification is helpful to assist farmers in their work for treatment on the plants and therefore could be quick and accurate to measure and eliminate the effects of diseases more profitably. In the past, the works were completed by naked-eye observation and basically relied on the experiences. Even so, the results are quite subjective and heuristic. In this paper, a mobile application to automatically classify several kinds of rice diseases from rice plant images and then to accurately recommend the uses of pesticides or chemicals. To do so, a proposed convolutional neural network (CNN) model is given. The results show that the proposed CNN model achieves the performance with the best trade-off between accuracy and time efficiency in comparison with the state-of-the-art models in our dataset. This model could be easily embedded into a mobile application to process in near real-time processing.
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