Rice leaf diseases pose a serious risk to rice management, subsequent in significant financial going belly up and jeopardizing food security. The purpose of this work is to compare the InceptionV3 analysis for rice leaf image disease detection. We test InceptionV3’s performance against various deep learning architectures and conventional machine learning models using a varied dataset of rice leaf photos. According to our findings, InceptionV3 outperforms other models in terms of accuracy. However, we also go into the advantages and disadvantages of InceptionV3 for rice leaf disease identification through thorough testing and comparisons.