Precision Agriculture Decision Support Using Machine Learning

The agricultural sector stands as the cornerstone of the Indian economy, with farmers often faced with the critical decision of selecting the most appropriate crop to cultivate based on multifaceted considerations such as profitability, market demand, soil quality, and climatic conditions. The ramifications of making suboptimal decisions can be profound, potentially exacerbating financial strain and even leading to tragic outcomes such as suicides. In light of these challenges, developing a robust system capable of offering predictive insights to Indian farmers regarding crop selection for specific seasons is imperative. To bolster decision-making and optimize resource utilization, this project presents a machine learning-powered agricultural decision support system. It tackles three crucial aspects: recommending the most profitable crop based on market demand, weather data, and soil analysis; suggesting the optimal fertilizer tailored to the chosen crop and environmental conditions; and detecting plant diseases through image analysis. This empowers Indian farmers with data-driven insights for improved crop selection, sustainable fertilizer use, and early disease identification, ultimately fostering agricultural productivity and farm income. Key Words: Precision agriculture, Recommendation system, Random Forest, Crop Recommendation, Fertilizer recommendation, Plant Disease detection, Machine Learning.