A Predictive Analytics Framework for Crop Recommendation Using Ensemble Learning Techniques

This paper presents the development and implementation of a data-driven Crop Recommendation System designed to assist farmers in making informed decisions regarding optimal crop selection. The system is deployed as a responsive web application featuring a user-friendly interface, allowing easy access and interaction for end-users. The application comprises three main modules: the Home Page, which introduces the system and outlines its significance; the User Guide Page, which provides step-by-step instructions for effective usage; and the Crop Recommendation Page, where users input specific environmental and soil parameters to receive crop suggestions tailored to their conditions.The core of the recommendation engine is a Random Forest Classifier, selected for its high accuracy and robustness in handling agricultural datasets. The model was trained on a comprehensive dataset that includes critical features such as nitrogen, phosphorus, potassium levels, temperature, humidity, pH, and rainfall. By analyzing these inputs, the system delivers personalized crop recommendations that align with the user's agro-climatic conditions, thereby enhancing decision-making for better agricultural outcomes.The model’s performance was evaluated using standard classification metrics, confirming its effectiveness in providing reliable crop suggestions. This intelligent system addresses a crucial need in modern agriculture for precision-based recommendations that can lead to increased crop productivity and resource optimization. The solution promotes sustainable farming practices by leveraging machine learning techniques and facilitating easy access through a web-based platform.