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First, the characteristics of the data provided by the agricultural sensor network were analyzed, including soil temperature, humidity, nutrient content, and other environmental parameters. On this basis, this study extracted useful feature information from multisource data, such as soil nutrient content, soil structure characteristics, and climate factors, and selected key features that had an important impact on land quality assessment through feature selection method. An automatic assessment model of watershed land quality based on machine learning algorithm was constructed, and appropriate model parameters were selected for optimization. The accuracy and stability of the model were verified by comparative experiments. The experimental results show that the model has excellent performance in [Formula: see text] value and other evaluation indexes, and can realize automatic and accurate evaluation of land quality in the basin. The automatic assessment model of watershed land quality based on multisource data fusion proposed in this study improves the accuracy and efficiency of land quality assessment.<\/jats:p>","DOI":"10.1142\/s0218126625504109","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T08:09:27Z","timestamp":1752221367000},"source":"Crossref","is-referenced-by-count":0,"title":["Multisource Data Fusion-Based Automatic Quality Evaluation Model for Cultivated Land in River Basins Under Agricultural IoT Networks"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3624-1836","authenticated-orcid":false,"given":"Yong","family":"Fu","sequence":"first","affiliation":[{"name":"Sichuan Institute of Nuclear Geological Survey, Chengdu, Sichuan 610061, P. R. 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