Rice Diseases Detection and Localization with Only Image-Level Disease Training Labels

In this paper, we propose a new approach for the detection and classification of rice diseases that combines the use of deep learning and level set methods. Our approach aims to improve the performance of rice disease classification and object detection by using an efficient image processing technique with deep learning. The proposed method will be evaluated based on Mean Average Precision (mAP), Accuracy, Precision, Recall, and F1-score. The results indicate that our proposed method improved the performance for most of the 16 classes of rice diseases and also it was able to improve performance even when using a small amount of labeled data. We also found that our proposed algorithm produced segmentation maps with significantly smaller computational time and the use of bias field estimation helped in the segmentation of complicated foreground objects in images by modifying unnecessary details. Overall, this research has the potential to be a valuable tool in the field of agriculture, where early and accurate detection of rice diseases is crucial for preventing the spread of the disease and maintaining crop yields.