Crop Yield Prediction based on Indian Agriculture using Machine Learning

Crop Yield Prediction based on Indian Agriculture using Machine Learning

Ashish Maurya1, Govind Yadav2, Chandrabhan Yadav3

1Ashish Maurya  CSE department ITM Gida Gorakhpur

2Govind Yadav CSE department ITM Gida Gorakhpur

3Chandrabhan Yadav CSE department ITM Gida Gorakhpur

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Abstract - Fertilizer use is typically under the limited control of farmers. For the farmers to achieve higher yields and reduce fertilizer loss, competent guidance is required for the best use of these fertilizers. Additionally, there is a connection between rainfall volume and nutrient loss for various fertilizer applications after each rainfall event. Rainfall that is moderate and falls at the right moment can help nutrients penetrate the soil's rooting zone and dissolve dry fertilizer. However, too much rain can increase the possibility of runoff and the pace at which nutrients like nitrogen (N) which is quint essential, phosphorus (P), and potassium (K) which are crucial, manganese (Mn), and boron (B) that are present in the soil. This research presents nutrient recommendations using an updated iteration of the random forest algorithm which is based on time-series data to forecast the required quantity of nutrients for various crops by examining rainfall patterns and crop fertility. The method suggested in this study, comes in handy for improving soil fertility by providing nutrients recommendations for optimum conditions for crop growth and reducing leaching and runoff potential.

Key Words:  Machine Learning, NPK Prediction, Proper Yield, Indian Agriculture, Random Forest.