查看文章

Rice is the primary food source for about half of the world's population, and it is cultivated around the world, especially in Asia. However, crop planting and farmers have faced various agricultural issues for centuries. The emergence of diseases that affect rice quality and production can result in crop losses. That is why the development of effective agricultural informatics tools is so important. This article focuses on the detection and the segmentation of various diseases that can affect the rice crop using deep learning techniques. This research explores a real-time, effective approach for the segmentation and detection of diseases in rice plants using a deep learning-based attention residual U-Net model. Data augmentation is performed to increase the model's effectiveness, and the model is trained from the scratch. We compared the performance of the proposed network with that U-Net and attention U-Net as …