Intelligent Systems for Crop Recommendation using Machine Learning

Given the soil and climate, information is of utmost importance in predicting which crop is best suited. Crops can now be grown with higher precision by analyzing data regarding temperature, humidity, soil conditions, and the chemical makeup of the soil, all of which impact crop growth. This is one facet of Precision Agriculture. Precision agriculture is a contemporary farming approach that uses scientific findings on the types, properties, and yields of soil. It guides farmers in selecting the most suitable crops tailored to their specific site conditions, reducing the chance of making unsuitable crop selections and ultimately helping raise overall productivity. The proposed work offers a web application that assists in classifying 22 crops based on various soil and environmental factors using two algorithms: SVM and Decision Trees. It analyzes the classifiers' accuracy using two performance metrics: the confusion matrix and the accuracy score. Farmers are better able to decide on the farming strategy they wish to use after utilizing the application.