This paper provides an in-depth analysis of various methods and concluding things for plant disease detection and classification, utilizing combinations of machine learning and deep learning approaches. The literature review highlights three primary methods: computer vision-based ML models, DL architectures, and spectroscopy-based techniques. While computer vision methods, including segmentation and manual feature extraction, have been extensively used, they are time-consuming and less effective for datasets with minimal visible symptoms. Deep learning, with its capability for automatic feature extraction, demonstrates significant potential, although challenges such as high computational costs and overfitting persist. Spectroscopy methods present alternative strategies, yet they are not universally applicable across all types of plant diseases. Our analysis suggests that optimized methodologies, capable of handling diverse datasets and overcoming the limitations of visual-based techniques, are essential for the timely and accurate classification of plant diseases. Future research must address these challenges to develop more robust, scalable, and efficient disease detection systems.