Rule-driven functional zoning index prediction for sustainable agricultural landscapes design based on multimodal fusion deep learning

Abstract This study develops a rule-based functional zoning index system for agricultural landscapes that integrates multi-source variables, including remote sensing spectral information, landscape structure, topographic conditions, crop configuration, and land cover. To enable automated prediction of the proposed index system, a Multi-Modal Landscape Fusion Network (MMLF-Net) model is developed. By integrating multi-source data, including remote sensing images, topographic features, crop structures, and land cover, the model constructs an end-to-end AL functional zoning system. Experimental verification is carried out in the Fresno region of California, the United States of America. The results show that MMLF-Net effectively improves the accuracy of functional identification and the stability of zoning, achieving an overall accuracy of 87.34% and a Kappa coefficient of 0.84. Among all functional types, the F1 scores for intensive production and natural fallow zones exceed 0.90, demonstrating the advantages of multimodal feature fusion in functional identification. Further landscape pattern analysis reveals several key structural characteristics, including overextended production-ecology interfaces, fragmented ecological patches, and complex morphologies within composite management zones. These findings provide a quantitative basis for ecological restoration layout and agricultural pollution prevention and control. This study aims to offer a practical spatial decision-making tool for the sustainable development of regional agriculture.