Gradient Boosting and Naive Bayes Crop Yield Prediction and Fertilizer Recommendation

Abstract

Farmers use Big Data to get information on changing Weather, Rainfall, Fertilizer Usage, Rainfall, and other factors that impact the crop yield. The yield of a crop is mainly determined by the climatic conditions like Temperature, Rainfall, Soil Conditions, and Fertilizers. All of this information assists farmers in making accurate and dependable decisions that maximize their productivity from cultivating the land. Recently, the Machine Learning Algorithms are used by the researchers to predict the yield of a crop before its actual cultivation. Firstly, Pre-process the data in a Python environment and then apply the Map Reduce Framework, which further analyses and processes the large volume of data. Secondly, K-means Clustering is employed on results gained from Map Reduce and provides a mean result on the data in terms of accuracy. Using Gradient Boosting Algorithm to predict the yield of crops based on the parameters like State, District, Area, Seasons, Rainfall, Temperature, and Area. To enhance the yield, this work 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 Naive Bayes [NB] Algorithm.