A Comprehensive Study on Crop Recommendation System for Precision Agriculture Using Machine Learning Algorithms

Abstract

Crop recommendation is among the most crucial components of the field known as precision agriculture. The crop recommendations were formulated after considering a wide range of distinct considerations. The term "precision agriculture" refers to a method of contemporary farming that makes use of information on soil features, soil kinds, and other factors, such as crop yields, and weather conditions to provide farmers with recommendations regarding the types of crops that would be most beneficial to grow on their farms to achieve the highest possible levels of both yield and profit. The crop recommendation system is currently being built with the use of machine learning methods including Random Forest, Gradient Boosting, XG Boost, Light GBM, SVM, and decision tree. The process of identifying the best crop to produce is aided by the information that consists of qualities such as potassium (K), phosphorous (P), and nitrogen (N) as well as temperature, humidity, pH, and rainfall. This application will be of assistance to farmers in enhancing agricultural output, minimizing the deterioration of soil on the cultivated ground, reducing the number of chemicals used in agricultural production, and increasing the productivity of cultivated land with available water resources. Also, this application recommends the top 5 prioritized crops to farmers. XG Boost achieved the best accuracy of 99.500 out of all these classifiers that were utilized in this paper. It also achieved the highest recall score of 99.545455, a precision score of 99.564935, and an F1 score of 99.545669.