RSF: Smart Farming Using Machine Learning-Based Recommendation System

Undoubtedly, agriculture and its associated sectors are the primary sources of income in rural areas of India. It is also an important contributor to the national GDP. The agricultural sector can grow with size and production technology supports the farmer crop cultivation on healthy soil. The crop yield per hectare is lamentable compared to global standards. In this research work, farmers propose a practical and user-friendly system, which can predict crop yield recommendation for use. Farmers access it through a proposed integrated system with machine learning and application interface. GPS: Global Positioning System (GPS) enables one to know his/her whereabouts. Hence, it is also possible to make a farm location on Google using this system. Users can input weather situations in the study area & soil health information required for predictive modelling. The model provides appropriate crops or anticipates yields on the farmer’s requests. Machine Learning approaches, Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to determine crop yield. The recommendation of the optimal machine-learning model is passed to the RSF: application interface for informed decisions about crop cultivation.