Soil Analysis for Crop Recommendation using Machine Learning Algorithms

In the area of farming, parts of the soil are of special note as it has to do with growing crops and provision of food for consumption. Soil analysis is also important in crop rating since it reflects some important Indices of the soils such as nutrients, pH, and texture which all affect plants' growing condition. Farmers are able to utilize this strategy in order to plant the right sorts of crops on their area of land, hence gaining scope in the yield and resources consumed. This paper showcases the application of advanced machine learning algorithms to optimize crop recommendations based on essential climatic, geographic, and eco-spatial parameters. In such techniques, depending on humidity, temperature and rainfall, certain patterns and relations are established and the best crops are recommended. For the purpose of generating predictive models that recommend the growth of specific crops in specific regions with the aim of increasing agricultural productivity, the system deploys the algorithms of Gradient Boosting, XGBoost, and LightGBM.