Smart Application for Early Detection of Rice Plant Disease Using Inception-ResNet-V2 with Ensemble Learning Framework

The use of the Inception-ResNet-V2 deep learning model and the LightGBM ensemble learning framework is proposed in this paper for the early detection of rice diseases. The system is trained on a labeled dataset of healthy and diseased rice plant images, enabling it to classify new images with high accuracy. This approach significantly enhances the ability to detect and respond to plant diseases, leading to improved crop yields and sustainable agriculture. The proposed smart application has the potential to greatly advance the accuracy and efficiency of disease detection in rice crops, benefiting both farmers and researchers. The findings from the experiments show that the suggested strategy is quite good at correctly identifying rice diseases, outperforming existing methods, and achieving high classification accuracy.