Intelligent Crop Selection System using Ensemble Learning with Random Forest Approach for Sustainable Farming

Crop analysis and prediction technology continue to expand rapidly because it serves as a critical tool for agricultural operation enhancement. In agricultural practices farmers depend on crop recommendations since these assists them in determining which crops their area and climate support most productively. Specialist knowledge operated as the main requirement in this process which demanded significant time consumption in the past. A sustainable way of living becomes necessary for continued existence. Agricultural experts believe machine learning automations should serve as a foundation for crop suggestion automation and pest detection to help farmers maximize their farming output with nutritious soil maintenance [1]. This paper develops a precise machine learning model which functions effectively for crop recommendation. A number of features within the proposed system utilize climatic data together with soil composition to make precise crop recommendations for specific areas. Agricultural crop recommendations would experience a transformative change due to this technology which leads to higher yields and sustainable farming alongside improved profitability for farmers at different scales. Through evaluation of many machine learning algorithms, we successfully measured perfect accuracy by running detailed tests over a large historical dataset. Our highest achieved accuracy level amounted to 99.5% accuracy.