A PSO-optimized fuzzy neural model for evaluating the coordinated development of agricultural economy and ecological protection

Abstract

The coordinated development of agricultural economy and ecological protection is essential for achieving sustainable agriculture, as the resulting ecological benefits have significant implications for both environmental security and economic stability. However, existing ecological benefit evaluation models often suffer from limited indicator coverage and insufficient intelligence in weight assignment, making it difficult to capture the coupled relationship between ecological and economic dimensions. To address these issues, this study proposes a comprehensive evaluation model based on fuzzy logic and a particle swarm optimized backpropagation neural network (PSO-FBP). A multi-level indicator system integrating ecological and economic value is constructed, and fuzzy logic is introduced to manage uncertainty, while PSO enables adaptive weight optimization. The proposed model demonstrates strong learning capability and robustness, enabling a comprehensive quantification of agricultural ecosystem services. A case study of a province shows that waste treatment, agricultural production, and soil conservation are the main contributors to ecological value, confirming the model’s effectiveness in real-world agricultural contexts. This research provides a scientific and practical tool for ecological benefit assessment, offering valuable support for decision-making in sustainable agricultural policy.