Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging

Rice, as a staple crop globally, requires proactive and accurate disease detection to ensure sustainable production. This study introduces a novel hybrid Deep Learning approach integrating thermal imaging and model hybridization for early and precise detection of rice leaf diseases. A dataset of 5,932 self-generated rice leaf images was augmented with simulated thermal images to capture subtle temperature variations indicative of early stress responses prior to visible symptoms. This novel use of thermal imaging enhances disease diagnosis efficiency and practicality. Eighteen Convolutional Neural Network (CNN) models were evaluated using transfer learning, with statistical analysis via Duncan's multiple range test (DMRT) identifying Darknet53 as the bestperforming model, achieving an accuracy of 95.79 %, sensitivity of 95.79 %, specificity of 95.93 %, and an F1 score of 0.96. To further improve performance, Darknet53 was hybridized by replacing its dense layer with a Support Vector Machine (SVM), resulting in significant enhancements. The hybrid model achieved 99.43 % accuracy, 99.43 % sensitivity, 99.81 % specificity, and an F1 score of 0.99. These results highlight the model's potential for real-time deployment in agricultural applications, providing an efficient and reliable solution for small-scale farmers. This research underscores the value of integrating thermal imaging with Deep Learning for advancing crop disease management and offers a framework for addressing other crop pathologies.