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In this paper, we propose a novel Belief Measure\u2010Weighted Fusion enhanced model that integrates Dempster\u2013Shafer evidence theory into mushroom recognition for the first time. Our enhanced model consists of two core components: a Probabilistic Classification Module with Parallel Color Representation that extracts multidimensional features through complementary color spaces and produces diversified soft predictions; and a multisource classification decision fusion (MCDF) Module that effectively reconciles and integrates conflicting evidence from these predictions. A key innovation within MCDF is the belief cosine similarity coefficient (BCSC), which quantitatively assesses inter\u2010evidence conflict, enabling an adaptive evidence fusion method that enhances robustness and decision reliability. Extensive experiments on a hybrid dataset containing challenging species show consistent performance gains across six mainstream deep networks, demonstrating strong generalization. This work not only bridges evidence theory with practical mushroom identification but also offers a transferable framework for recognizing visually similar species in wild food contexts.<\/jats:p>","DOI":"10.1002\/cpe.70520","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T04:15:27Z","timestamp":1766981727000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Model for Edible Mushroom Recognition Based on Belief Measure\u2010Weighted Fusion"],"prefix":"10.1002","volume":"38","author":[{"given":"Shuai","family":"Yang","sequence":"first","affiliation":[{"name":"College of Science Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Science Northeast University  Shenyang 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