SOWEASY CROP AND FERTILIZER RECOMMENDATION SYSTEM

Agriculture is a critical sector that supports global food security and economic development. However, traditional farming methods often rely on experience-based decision-making, which can lead to inefficiencies in crop selection and fertilizer application. The SOWEASY Crop and Fertilizer Recommendation System leverages advanced technologies such as machine learning (ML), artificial intelligence (AI), and big data analytics to provide farmers with data-driven insights for optimal agricultural productivity. This system integrates soil analysis, climate assessment, and historical agricultural trends to recommend the most suitable crops and fertilizers for specific farm conditions. By evaluating soil nutrient levels, pH balance, organic matter content, and climate factors such as temperature and rainfall, the system ensures precision in resource management. It helps farmers minimize input costs, reduce environmental impact, and enhance crop yields. Additionally, AI-powered predictive modeling aids in identifying potential risks such as nutrient deficiencies, pest infestations, and climate variability, allowing proactive decision-making. The implementation of this system promotes sustainable agriculture by preventing soil degradation, reducing chemical fertilizer overuse, and improving soil health. Small-scale farmers, who often lack access to expert agricultural guidance, can benefit from mobile and web-based applications that provide tailored recommendations. While current challenges such as data accessibility and technological adoption exist, future advancements in AI, IoT, and remote sensing will further enhance the precision and efficiency of recommendation systems. By integrating modern technology with agriculture, the SOWEASY Crop and Fertilizer Recommendation System aims to revolutionize farming practices, ensuring food security, economic stability, and environmental sustainability. This paper explores the importance, functionality, and benefits of this system while highlighting its role in transforming traditional agriculture into a data-driven, precision-based industry. KEYWORDS Crop Recommendation System, Fertilizer Recommendation, Precision Agriculture, Machine Learning in Agriculture, Artificial Intelligence (AI), Soil Analysis, Climate Assessment, Sustainable Farming, Data-Driven Agriculture, Big Data Analytics, Nutrient Management, Smart Farming, Agricultural Technology, Environmental Sustainability, Predictive Analytics in Agriculture.