Recommendation System to Precision Agriculture Using Machine Learning Algorithm

Abstract

The Indian economy heavily relies on agriculture, a vital source of livelihood for millions of farmers and a significant contributor to the GDP. However, farmers often face challenges in selecting the most suitable crops for their specific soil and environmental conditions. This decision profoundly influences crop productivity and the overall agricultural industry. Addressing this issue is crucial, and crop prediction plays a pivotal role in the emerging concept of precision agriculture. Precision agriculture seeks to streamline the crop selection process by leveraging machine learning techniques. The model takes into account various factors such as soil type, rainfall patterns, temperature, groundwater levels, availability of pesticides and fertilizers, and the current season. These elements are intricately analyzed to create a robust recommender system. The ultimate objective is to expedite and enhance the crop selection procedure for farmers. By incorporating data-driven insights, this precision agriculture approach aims to empower farmers with informed decisions tailored to their unique agricultural landscapes. The recommender system acts as a valuable tool, offering guidance on the most suitable crops for optimal yield and sustainability. This initiative not only addresses the challenges faced by Indian farmers but also contributes to the overall efficiency and resilience of the agricultural sector. In summary, the integration of machine learning and data analytics in crop prediction for precision agriculture holds the potential to revolutionize decision-making processes and positively impact crop productivity in the Indian agricultural landscape.