Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach.

Improving soil health evaluation and crop output forecasting are essential for developing sustainable agricultural methods. By applying data-driven insights, farmers may optimize resources, increase productivity, and promote environmental sustainability. The intricacy of environmental conditions and the lack of access to trustworthy data make it difficult to anticipate crop yields and evaluate soil health accurately. The goal of this research is•to make sophisticated models for precise crop production forecasting and thorough evaluation of soil health,•to improve sustainability by optimize farming methods, and•to assist farmers in making well-informed decisions.Iterative Partitioning-Ensemble Filter (IP-EF) is a technique used for feature selection, enhancing model performance by iteratively partitioning data and refining feature subsets. Back-propagation Neural Network (BPNN) is an SL algorithm applied for predicting complex patterns, especially in crop yield and soil health assessments. Multi-Source Data Fusion-Geographical Information Systems (MSDF-GIS) combines diverse data sources with GIS to map and analyze spatial agricultural data, improving decision-making for sustainable farming practices. These methods collectively optimize prediction accuracy and resource management. The result shows that the suggested model significant improvement in precision, recall, and F1-Score for crop yield, reaching 93 %, 94 %, and 93 %, implemented using Python software. Future advancements may include real-time monitoring, integrating AI by IoT expedients for dynamic decision-making, and enhancing sustainability by minimizing water usage, fertilizers, and environmental impact in agriculture.