Krashignyan: A Farmer Support System

Abstract

Agriculture is the primary component of the Indian economy. It is the primary source of food supply and is essential to our livelihoods. The majority of Indians rely on agriculture for their employment. Agriculture production declines as a result of unpredictable weather, wrong selection of crops, unbalanced fertilizer use, and a lack of market awareness. Farmers face numerous challenges in traditional farming, and many times, farmers fail to select the appropriate crop for cultivation. Crop growth is affected by a variety of factors such as weather, soil parameters, and fertilizers. A crop recommendation system is proposed in this paper to assist farmers in selecting the appropriate crop based on the location, weather data, crop sowing season, and soil parameter. Various Machine Learning techniques, such as Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes, and XGBoost Classifier methods, were used for recommendation. The XGBoost classifier gives the best results with a 97% accuracy, hence the final model was developed using the XGBoost classifier. This system will help farmers in selecting the best crop for their fields while increasing agricultural yield.