Abstract : Agriculture remains a primary yet underpaid occupation in India, playing a crucial role in the nation's economic development.Withapproximately70% ofthe Indian economy dependent onagriculture,cropdamage canresult in significant productivity losses, thereby impacting the economy as a whole. Continuous monitoring of crops from the initial stages of their life cycle throught oharvestis essentialtopreventsuchlosses.Plantdiseases pose a serious threat not only to farmers but also to consumers, the environment, and the global economy. The application of machine learning in agriculture has the potential to revolutionize the field by detecting plant diseases early and suggesting appropriate fertilizers to farmers. In this study, we focus on detecting diseases in four key crops: chilies, cotton, rice, and tomatoes. By utilizing deep learning techniques, this approach aims to accurately identify and classify various plant leaf diseases based on image analysis. In the agricultural sector, technology is increasingly being used to assess leaf quality through methods such as classification, image processing, and image acquisition. Machine learning algorithms offer predictive insights that can assist farmers incultivatingcropswithenhancedqualityandyieldbyidentifyingplantdiseasesintheearlystagesoftheirgrowth cycle and providing fertilizer recommendations.