{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:34:59Z","timestamp":1787027699642,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Province Tea Industry Artificial Intelligence and Big Data Application Innovation Team","award":["202405AS350025"],"award-info":[{"award-number":["202405AS350025"]}]},{"name":"Yunnan Province Smart Tea Industry International Joint Laboratory","award":["202403AP140022"],"award-info":[{"award-number":["202403AP140022"]}]},{"name":"Yunnan Province Tea Industry Technology Innovation Center","award":["202505AK340010"],"award-info":[{"award-number":["202505AK340010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye\u2019s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates\u2019 \u201cmulti-scale parallelism\u2014global modulation\u2014long-range integration,\u201d multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network\u2019s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems.<\/jats:p>","DOI":"10.3390\/biomimetics11010056","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T14:32:38Z","timestamp":1767882758000},"page":"56","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on Drought Stress Detection in the Seedling Stage of Yunnan Large-Leaf Tea Plants Based on Biomimetic Vision and Chlorophyll Fluorescence Imaging Technology"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2102-411X","authenticated-orcid":false,"given":"Baijuan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Agricultural & Biotechnology, Zhejiang University, Hangzhou 310013, China"},{"name":"College of Tea Science, Yunnan Agricultural University, Kunming 650201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Tea Science, Yunnan Agricultural University, Kunming 650201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxue","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Tea Science, Yunnan Agricultural University, Kunming 650201, China"},{"name":"Mechanical & Electrical Engineering College, Wuhan Donghu College, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2315-8612","authenticated-orcid":false,"given":"Jihong","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Agricultural & Biotechnology, Zhejiang University, Hangzhou 310013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujuan","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Tea Science, Yunnan Agricultural University, Kunming 650201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5781-7771","authenticated-orcid":false,"given":"Shihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Mechanical & Electrical Engineering College, Wuhan Donghu College, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0278-4625","authenticated-orcid":false,"given":"Yuefei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Agricultural & Biotechnology, Zhejiang University, Hangzhou 310013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"ref_1","first-page":"1066","article-title":"Realization of sustainable development goals in the tea industry: A case study of Lincang City, Yunnan Province","volume":"37","author":"Zhao","year":"2022","journal-title":"Adv. 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