Decision Support System for Crop Planning and Agricultural Health using Machine and Deep Learning Methods

In order to ensure economic stability and food security, agriculture plays a vital role. However, farmers often find it difficult to make well considered decisions regarding disease detection with appropriate fertilizer or supplement suggestions for the detected disease, crop selection on the basis of rainfall soil health and location specific conditions. The Decision Support System for Crop Planning and Agriculture Health using Machine and Deep Learning Methods is a web application developed by utilizing a python framework named Flask is introduced in this study which employs Deep and Machine Learning models to assist farmers with three essential tasks: Disease detection with appropriate fertilizer or supplement suggestion for the detected disease, crop suggestion, and supplements and fertilizer purchasing section for known crop condition. Results from experiments prove the system's effectiveness in a range of agricultural domains, assisting farmers in boosting crop yield and ensuring timely disease remedy. This well-rounded strategy presents a feasible framework for revolutionizing modern farming practices. This Decision Support System ensures food security, pest control, crop production, better crop yield, better agriculture, economic growth, and good technology for farmers.