Clustering Analysis of Integrated Rural Land for Three Industries Using Deep Learning and Artificial Intelligence

Abstract

This study employs deep learning and artificial intelligence (AI) clustering analysis techniques to evaluate the suitability of integrated rural land for three industries. Diverse datasets pertaining to rural development, encompassing land use, agricultural production, and rural tourism, are gathered and harmoniously amalgamated. An innovative land suitability assessment model, merging ResNet-50 with the k-means algorithm, is devised. Specifically, ResNet-50 is harnessed for the classification and recognition of rural land-use images, thus deriving feature vectors for each sample. These feature vectors are subsequently fed into the k-means algorithm to cluster samples with akin land-use patterns. The ensuing examination of land use composition within each cluster facilitates the evaluation of rural land's suitability for three-industry integration. Experimental scrutiny discloses that this study achieves an accuracy rate of 88.3% in rural landuse classification and recognition, outperforming alternative algorithms by at least 3.1%. Furthermore, it yields an average intersection over union (IoU) of 67.29%. Remarkably, the k-means algorithm exhibits superior clustering outcomes. Consequently, the model introduces herein demonstrated substantial enhancements in rural land-use classification and recognition accuracy, average IoU, and clustering performance. It offers an innovative tool for policymakers to advance rural industry integration, fostering economic diversification. Additionally, this model aids decision-makers in identifying prospective opportunities and challenges, thus facilitating the formulation of forward-thinking and viable rural development strategies.