RiceNet: a robust ensemble attention mechanism for automated rice plant disease classification

Abstract Rice is a widely cultivated crop in Asia and is paramount in ensuring national and global food security. However, rice plants are susceptible to various diseases that negatively impact crop quality and quantity to meet the needs of the world’s growing population. Automated rice plant disease classification ensures food security and agricultural sustainability. Although traditional deep learning approaches have shown promising results in rice plant disease classification, the challenges posed by the heterogeneity of the data set and the feature imbalance persist. This research introduces a robust and novel Ensemble Attention Mechanism (EAM) that uses fine-tuning transfer learning to address these challenges and pre-trained (VGG16, VGG19, and customized ResNet called RiceNet, which comprises ResNet18 and ResNet50) as baseline models, specifically tailored to improve rice plant disease classification within heterogeneous datasets. The main contribution of this paper is to introduce a RiceNet framework that incorporates ensemble learning principles and attention mechanisms to adaptively balance data heterogeneity and feature representation by effectively integrating every representation to mitigate inherent class distribution imbalances. Comprehensive ablation studies validate the effectiveness of each component in the framework, demonstrating significant improvements in classification performance compared to traditional methods. Furthermore, the evaluation of RiceNet on two extensive publicly available datasets (close environment and field environment) shows its superior performance, achieving an impressive F1 score of 100% and a balanced precision of 100% on both large and small datasets. This research sets a new benchmark for rice disease classification and provides a versatile framework applicable to agricultural precision, contributing to food security and sustainability.