A Machine Learning-based Approach for Crop Yield Prediction and Fertilizer Recommendation

Agriculture plays a critical role to Indian global economy and contributes a major part to GDP. With the expansion of the human population, it is necessary to maintain food security, it is achieved and controlled by the agricultural yield produced by the nation. The yield of a crop is mainly determined by the climatic conditions like temperature, rainfall, soil conditions, and fertilizers. Due to these variable factors, the production gets affected and remains a huge problem for the agricultural sector to strengthen the need for exactness for proper analyzing the crop production in variable climatic conditions. Recently, the machine learning algorithms are used by the researchers to predict the yield of a crop before its actual cultivation. This research study has proposed a machine learning algorithm: AdaBoost to predict the yield of crops based on the parameters like state, district, area, seasons, rainfall, temperature, and area. To enhance the yield, this research study also suggests a fertilizer based on the soil conditions like NPK values, soil type, soil PH, humidity, and moisture. Fertilizer recommendation is primarily done by using the Random Forest [RF] algorithm.