A Machine Learning Approach to Crop Recommendation, Plant Disease Identification, and Yield Estimation

The reduction in farm output can be linked to three major causes, i.e., plant diseases, improper crop choices, and wrong yield estimates. The remedy calls for an integrated artificial system that merges disease identification, crop suggestions, and yield estimation functionalities. The proposed system proves to be highly effective in identifying infected plant types and disease types by employing early disease detection with convolutional neural networks (cnns) on the plant village dataset. The crop suggestion model employs Random Forest (RF) as the primary algorithm, prioritizing its performance optimization over methods like k-nearest neighbors (KNN), logistic regression, and decision trees. The crop recommendation helps the farmers to decide on proper crops by looking into their nitrogen, phosphorus, potassium contents in the soil, pH level, and water content. The system utilizes a decision tree model to forecast crop yield, drawing on historical data related to agriculture and weather conditions. The mode enables accurate estimation of yield, which can help the farmer plan effectively and optimize his use of resources. All components and evaluation algorithms exhibited exceptio