A Hybrid Deep Learning Approach for Rice Plant Disease Detection

The agricultural sector necessitates automated identification and analysis of rice diseases to conserve financial and other resources, mitigate yield loss, enhance processing efficiency, and secure healthy crop harvests. Rice is an essential commodity for global food security; however, it is very vulnerable to numerous illnesses that can markedly diminish productivity. Timely identification and precise forecasting of these diseases are crucial for reducing losses. Conventional image-based disease detection techniques frequently utilise Convolutional Neural Networks (CNNs) to extract spatial information; however, they inadequately account for the temporal evolution of diseases, which is essential for efficient monitoring and diagnosis. This research proposes a hybrid model that integrates a Hierarchical Convolutional Recurrent Neural Network (HCRNN) with Long Short-Term Memory (LSTM) networks for the prediction of rice plant illnesses. The HCRNN extracts multi-scale spatial characteristics from rice plant pictures, whilst the LSTM network models temporal relationships in disease progression, hence augmenting predicting capabilities. This integrated methodology enhances performance by integrating spatial and temporal information. We assessed the model using a dataset of rice plant leaf pictures impacted by multiple diseases, including bacterial leaf blight, blast, and sheath blight. The proposed model exhibited enhanced performance, with an accuracy of 98.5%, above that of conventional CNN-based models. This method also resolves the challenge of limited datasets by accurately tracking disease development across time. The findings indicate that the integration of HCRNN with LSTM establishes a resilient framework for predicting rice diseases. The suggested approach is adaptable to additional crops and disease categories, providing a scalable solution for precision agriculture and disease management. Subsequent efforts will concentrate on incorporating environmental variables, including soil and meteorological data, to augment predictive accuracy.