The chapter explores the popular machine learning and deep learning approaches in the realm of agriculture which have proven their strength in the classification and detection of various plant and leaf diseases. Plants are the basic fundamental for food supply. It is essential to identify the various diseases that can decrease the growth of plants and cause an impact on the food supply. The chapter focuses on and discusses various applications and limitations of these popular algorithms and models which help for the categorization of disease and help the farmers and researchers to have a basic understanding of the models to create improved models for the research by utilizing the discussed model learning. We highlight the benefits of these computational models by showing how well they can handle large datasets and precisely identify diseases. We also discuss the drawbacks of these models, such as the need for large datasets for model training and potential issues with generalizability across different crop species or environmental conditions. The objective of this chapter is to provide an in-depth analysis of the most recent technological advancements, their effects on agriculture, and the direction that research and implementation are taking.