TY - JOUR
T1 - An Improved CNN–BiGRU Framework for Rice Disease Identification Using Attention Mechanisms
AU - Ali Hassan
AU - Noor Siddiqui
AU - Bilal Khan
AU - Sana Malik
AU - Hassan Ali
JF - Journal of Intelligent Agriculture and Food Systems
Y1 - 2025
VL - 4
IS - 1
SP - 110
N2 - Accurate identification of rice diseases in precision agriculture using imagery remains difficult due to substantial misclassification rates, a wide range of influencing factors, and fluctuating conditions. While machine learning together with convolutional neural networks has yielded encouraging results for detecting rice diseases, these approaches have struggled to elucidate the interconnections among disease characteristics. In this work, we introduce an enhanced classification framework for rice diseases that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU). Concretely, a residual connection was embedded into the Inception module, its depth was enlarged, and a refined Convolutional Block Attention Module (CBAM) was incorporated. The upgraded CNN and the BiGRU were trained and evaluated separately; their outputs were then concatenated and forwarded to the classification layer for final prediction. Trials confirm that this strategy achieves 98.21% classification accuracy across four rice disease categories, delivering a robust solution for rice disease detection research.
UR - https://airpublications.com/c456215945
ER -