Crop Recommendation using Ensemble Stacking Machine Learning approach

Agriculture bears an impressively predominant position in the process of india’s financial system expansion. In india, agricultural sector generates more employment opportunities than any other field. It constitutes 50% of the overall workforce of our country. Due to their less knowledge and ignorance of the various soil components and environmental factors, farmers choose the wrong crop for cultivation, which has a significant negative impact on agricultural productivity. The development of a system which provide a scientific approach to assist the farmers in forecasting the plentiful crops to be sowed based on several factors that affect the overall production is necessary to remove this barrier. Crop forecasting is essential for improving farming methods, reducing risks, assuring food security, and advancing sustainable farming. Across the agricultural value chain, it facilitates informed decision-making and effective resource allocation by offering insightful information about crop yields and production. This work utilize a data set that consists of soil parameters such as Nitrogen(N), Phosphorous(P), Potassium(K) and ph of the soil and weather parameters such as temperature, rainfall and humidity for 22 crops and applied ensemble stacking machine learning techniques to recommend crops with high accuracy and efficiency. Therefore, it can be beneficial for farmers to have greater versatility.