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KRUSHI-MITRA : A Study on Machine Learning Algorithms for Precision Farming

Authors:

Mr. Sangram Mahangare, Prof. Dipali Bhusari

PG Student, Department of MCA, Trinity Academy of Engineering Pune, India Guide, Department of MCA, Trinity Academy of Engineering Pune, India

ABSTRACT: The demand for creative solutions to improve farming operations grows as the agricultural landscape continues to change. In order to provide farmers with personalized and data-driven crop recommendations, this research provides a Crop Recommendation System based on Artificial Intelligence and Machine Learning. The main objective is to promote more sustainable and effective farming methods, taking into account the particular difficulties farmers encounter in maximizing crop production. The suggested method makes use of cutting-edge AI and ML algorithms to examine various agricultural data sets, including soil properties, climatic trends, past crop performance, and other pertinent factors. The system uses cutting edge approaches like ensemble methods, decision trees, and predictive modeling to deliver precise and customized crop selection suggestions depending on the unique circumstances of each farm. Evaluation metrics, such as accuracy, recall and precision, are used to carefully evaluate the Crop Recommendation System’s performance. Analyses conducted in comparison with baseline models demonstrate the efficacy and superiority of the suggested AI and ML-based method in terms of producing precise and trustworthy crop recommendations.