CROP YIELD AND FERTILIZER PREDICTION USING MACHINE LEARNING

Computer Engineering

In India, agriculture is the primary industry for earning a living. 70% of the Indian workforce is employed directly in Agriculture. The most frequent problem faced by young Indian Farmers is choosing the right crop depending on the needs of the land. They are consequently seeing a marked decline in Productivity. Agriculture is now seriously threatened by climate change and other environmental modifications. One essential strategy for handling this issue practically and successfully is machine learning. Use historical information from the weather, soil, rainfall, and crop yields to predict agricultural yields. We used machine learning algorithms to achieve this. Linear Regression, ANN, K Nearest Neighbors, Random Forest, SVM, and other methods were thought of, but the SVM was selected with an accuracy of 86%. In this project, a web application that can estimate both overall and individual crop yields is developed. In addition, it tells the user when fertilizer should be applied.

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