Abstract

In today's agricultural landscape, farmers face numerous challenges in selecting the right crops to plant, primarily due to varying soil conditions and unpredictable weather patterns. This often leads to suboptimal yields and inefficient use of resources. To tackle these issues, we propose a Crop Recommendation System powered by machine learning, specifically utilizing the Random Forest algorithm. This innovative system will analyze essential factors such as soil nutrients—nitrogen, phosphorus, and potassium—as well as climatic conditions like temperature, humidity, and rainfall, to provide tailored crop recommendations. Our goal is to empower farmers by simplifying the decision-making process, ensuring they have access to reliable guidance for improving crop selection. By making informed decisions, farmers can boost their yields, enhance sustainability, and ultimately contribute to a more efficient agricultural sector. This project not only aims to improve individual crop management practices but also supports researchers and policymakers in developing better agricultural strategies. Through this initiative, we hope to pave the way for a future where technology and agriculture work hand in hand for a more sustainable and productive world.