Comprehensive Analysis of Deep Learning based framework for Automated Plant Disease Detection and Classification to Enhance Sustainable Agriculture

The rapid advancement of deep learning has revolutionized various domains, including smart agriculture, where it plays a pivotal role in plant disease detection. The purpose of the article is to highlight deep learning-based approaches for detecting different types of plant diseases. The article covers various types of plants, their associated diseases, and the importance of addressing these issues. The detailed literature and comparative analysis demonstrate how different datasets have been explored for plant disease detection and how various deep learning techniques have been utilized for predictions. The key challenges, along with a detailed study in the comparative analysis, also highlight the future scope for further research in this field and the limitations of each study discussed in the article. The objective of the present research is to propose an effective deep learning-based mechanism for detecting different types of plant diseases, enabling accurate large-scale development in the field of smart agriculture for sustainability.