Crop Recommendation System using Machine Learning Algorithm

Developing automated agricultural practices presents many challenges. Especially in India, with its range of terrains including mountains, desserts, rivers, plains and grasslands. Extensive research has been done in the field of farming but determining the best crop for cultivation on a particular land is still not an easy task. Due to the lack of advanced machinery the systems developed are not capable of properly analyzing environmental factors. This research paper offers a machine learning algorithm to develop a recommendation system for crops. In this research paper, we are going to compare different machine learning algorithms such as Random Forest, Decision Trees, Support Vector Machine, Naïve Bayes, and XGBoost (Gradient Boosting) to suggest the crop which is suitable for a particular piece of land and environmental factors. Additionally, the system can assist in identifying crops suitable for non-farming lands, which differ in environmental conditions such as water levels, soil nutrient levels, sunlight exposure, and moisture. Such a recommendation system holds promise in addressing the challenges associated with cultivating crops on diverse terrains and non-traditional farming areas.