Comprehensive Review of Deep Learning Models for Plant Disease Detection with a Focus on Apple Leaf Pathologies

Effective agriculture and the economy are the identification of plant diseases. Conventional means for diagnosing plant disease are laborious since they call for a lot of effort, time, and knowledge. Recently, academics, researchers, and practitioners have given a lot of attention to an important study topic automatic disease spotting of plants. Like a flash, diseases first manifest itself on plant leaf, Machine learning and automated analysis can assist in recognizing it. This cutting-edge review has conducted detailed research to assess the viability of applying AI based models to categorize plant disorders. The pathogens and infections of various crops are examined in this study. A comprehensive study of the many procedures required in classifying and detecting using ML and AI to diagnose plant diseases is presented. Additionally, several online databases for identifying plant diseases have been given. In addition to this, a thorough analysis of a variety of currently used classifying models based on ML and DL proposed by many researchers for different crops is explained. This includes information on how well each model performed, the dataset it was based on, and the technique used to extract features. Lastly, a variety of concerns are highlighted and examined when employing DL & ML to predict plant pathogens.