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Zool."],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Nowadays, infrared camera technology has become an important tool for large\u2010scale monitoring and assessment of wildlife and has generated a huge amount of image data, but how to quickly identify wildlife species has become a major problem nowadays. To solve this problem, this study proposes MWAC, a model for accurate classification of wildlife species in infrared camera images by integrating image and text descriptions. First, we propose a wavelet KAN module (WDS\u2010KAN) for multi\u2010scale feature extraction of wildlife images, which enables the model to capture the subtle changes in animal images more accurately. Second, a label\u2010correlated text extraction module (LC\u2010BERT) is proposed to capture label co\u2010occurrence patterns and hierarchical relationships in the dataset, which enhances the generalization ability of the model. Finally, a multi\u2010strategy coati optimization algorithm (MSCOA) is proposed, which enhances our ability to globally search for the optimal solution, effectively avoids falling into local optima, and accelerates the model training speed. In order to verify the effectiveness of the model, we constructed a dataset containing 10 species of wild animals. The experimental results show that the MWAC model achieves 97.53% in accuracy, 97.33% in precision, 95.84% in recall, and 97.51% in\n                    <jats:italic>F<\/jats:italic>\n                    1, which are better than those of the existing models, indicating that the model is good for the rapid identification of wildlife and other aspects of good results and can be applied to large\u2010scale wildlife monitoring and assessment.\n                  <\/jats:p>","DOI":"10.1111\/1749-4877.70184","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T13:43:59Z","timestamp":1790862239000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Wildlife Multi\u2010Modal Recognition Method Based on Infrared Camera Images Using MWAC"],"prefix":"10.1111","author":[{"given":"Siming","family":"Deng","sequence":"first","affiliation":[{"name":"Central South University of Forestry and Technology  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3590-5966","authenticated-orcid":false,"given":"Guoxiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Central South University of Forestry and Technology  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Central South University of Forestry and Technology  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhishu","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture Institute of Zoology, Chinese Academy of Sciences, Beijing, China, and University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqun","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture Institute of Zoology, Chinese Academy of Sciences, Beijing, China, and University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"Central South University of Forestry and Technology  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Gui","sequence":"additional","affiliation":[{"name":"Central South University of Forestry and Technology  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"issue":"7","key":"e_1_2_11_2_1","doi-asserted-by":"crossref","first-page":"10445","DOI":"10.1007\/s11042-022-13629-x","article-title":"SWIN Transformer Based Contrastive Self\u2010Supervised Learning for Animal Detection and Classification","volume":"82","author":"Agilandeeswari L.","year":"2022","journal-title":"Multimedia Tools and Applications"},{"issue":"15","key":"e_1_2_11_3_1","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.3390\/electronics11152388","article-title":"MobileUNetV3\u2014A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation","volume":"11","author":"Alsenan A.","year":"2022","journal-title":"Electronics"},{"key":"e_1_2_11_4_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.atech.2022.100163","article-title":"Multimodal Sensor Data Fusion for In\u2010Situ Classification of Animal Behavior Using Accelerometry and GNSS Data","volume":"4","author":"Arablouei R.","year":"2023","journal-title":"Smart Agricultural Technology"},{"key":"e_1_2_11_5_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125157","article-title":"A High\u2010Quality Self\u2010Supervised Image Denoising Method Based on SDDW\u2010GAN and CHRNet","volume":"258","author":"Chen Y.","year":"2024","journal-title":"Expert Systems With Applications"},{"key":"e_1_2_11_6_1","unstructured":"Chu X. 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