Soil-Based Crop Recommendation System Using Machine Learning

The crop recommendation systems in agriculture frequently depend on general guidelines or personal experience, with little regard for important soil factors such as pH and nutrient concentrations. It also results in poor outcomes and resource inefficiencies. In response, the study introduces a Soil Based Crop Recommendation System (SBCRS), which integrates machine learning techniques to deliver tailored crop recommendations based on soil parameters. The SBCRS collects extensive soil and crop performance data, extensively preprocesses it, gathers relevant features, and then trains and evaluates models using machine learning methods. The system exceeds existing systems by achieving 93% accuracy, 89% precision, 93% recall, and a 90% F1 score, as opposed to the existing system's 78% accuracy and 86% F1 score. Furthermore, the study emphasizes the significance of soil pH levels in evaluating crop compatibility, providing precise information about soil conditions and their implications for agricultural activities. Overall, the SBCRS offers a data-driven approach to crop selection that improves farm productivity and sustainability while resolving the limitations of existing recommendation systems.