{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T16:29:33Z","timestamp":1786638573142,"version":"build-2736575974"},"reference-count":53,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":25,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11705068"],"award-info":[{"award-number":["11705068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32202219"],"award-info":[{"award-number":["32202219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Physiologia Plantarum"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Accurate detection of tomato ripeness and size is critical for robotic thinning and harvesting but remains challenged by performance degradation in adverse weather, imprecise size estimation, and computational constraints on edge devices. To bridge this gap, we introduced (1) the TIDAW dataset (Tomato Images in Diverse Adverse Weather), synthetically generated via a physically\u2010grounded atmospheric scattering model to simulate realistic rain and fog; and (2) Edge\u2010YOLO\u2010Tomato, a novel YOLOv8\u2010based architecture, featuring four key innovations: a physics\u2010aware scattering module that unifies multi\u2010particle light transport theory with dual\u2010attention mechanisms to explicitly model wavelength\u2010dependent scattering for robust feature disentanglement; dilated convolutions enhancing receptive fields; a prior\u2010embedded Wise\u2010IoU loss incorporating botanical size distribution priors to rectify bounding box bias; and a compression framework that combines magnitude pruning and layer\u2010wise pruning using neural architecture search. Extensive evaluations demonstrate leading performance: Edge\u2010YOLO\u2010Tomato achieves 93.3% mAP\n                    <jats:sub>50<\/jats:sub>\n                    and 74.3% mAP\n                    <jats:sub>50:95<\/jats:sub>\n                    on TIDAW, surpassing YOLOv8, YOLOv11, Faster R\u2010CNN, and RT\u2010DETR etc. by 1.1%\u201326.3% and 0.2%\u20132.2%, respectively. The compressed model attains a 4.7373\u2009MB footprint (20.58% size reduction) with \u2266\u20090.5% accuracy loss and delivers 50% latency reduction on CPU. This work establishes a new paradigm for vision\u2010based precision agriculture by unifying physical data synthesis, physics\u2010aware modeling, and compression framework, enabling real\u2010time robust fruit detection in uncontrolled environments. The codes are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/YLu567\/Edge-YOLO-Tomato\">https:\/\/github.com\/YLu567\/Edge\u2010YOLO\u2010Tomato<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/ppl.70764","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T06:13:42Z","timestamp":1769494422000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Edge Device\u2013Oriented Tomato Fruit Thinning and Harvesting Model Under Adverse Weather Conditions"],"prefix":"10.1111","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2328-3107","authenticated-orcid":false,"given":"Yalu","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Physics, School of Science Jimei University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5600-232X","authenticated-orcid":false,"given":"Maolan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Physics, School of Science Jimei University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0892-8227","authenticated-orcid":false,"given":"Zhenchang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology ShanghaiTech University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1870-8061","authenticated-orcid":false,"given":"Honghao","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Physics, School of Science Jimei University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5827-1717","authenticated-orcid":false,"given":"Hui","family":"Ni","sequence":"additional","affiliation":[{"name":"Xiamen Ocean Vocational College  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Agossadou E. 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Y.Wang M.Wei F. L.Wang andH.Xie.2024.\u201cFriendNet: Detection\u2010Friendly Dehazing Network.\u201darXiv PreprintarXiv:Abs\/240304443.https:\/\/doi.org\/10.48550\/arXiv.2403.04443."},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.12133\/j.smartag.SA202309010"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/1750\u20103841.17159"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10499\u2010024\u201001422\u20106"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/horticulturae11010084"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-4268-2_3"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263\u2010024\u201002281\u20107"},{"key":"e_1_2_11_17_1","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Han S.","year":"2016"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1506.02626"},{"key":"e_1_2_11_19_1","unstructured":"Hosen M. andM. H.Islam.2025.\u201cAggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management.\u201darXiv PreprintarXiv:Abs\/250112052.https:\/\/doi.org\/10.48550\/arXiv.2501.12052."},{"key":"e_1_2_11_20_1","first-page":"1","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Howard A. 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