RiceDRA-Net: Precise Identification of Rice Leaf Diseases with Complex Backgrounds Using a Res-Attention Mechanism
Source abstract
Abstract
In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, F1 score, and confusion matrix in both cases, demonstrating its strong robustness and stability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Plant Virus Research Studies · Plant Disease Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Funding
National Key R&D Program of China
References (40)
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