{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:11:25Z","timestamp":1781878285529,"version":"3.54.5"},"reference-count":31,"publisher":"Zhejiang University Press","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Zhejiang Univ. Sci. B"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1631\/jzus.b2500647","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T01:00:54Z","timestamp":1779498054000},"page":"466-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Embedding of ripening topology into one-stage detection for tomato cluster phenotyping","\u5c06\u6210\u719f\u62d3\u6251\u5e8f\u5217\u5d4c\u5165\u5355\u9636\u6bb5\u68c0\u6d4b\u7528\u4e8e\u4e32\u756a\u8304\u8868\u578b\u5206\u6790"],"prefix":"10.1631","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2319-4454","authenticated-orcid":false,"given":"Bingquan","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyuan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochuan","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zishun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5610-4448","authenticated-orcid":false,"given":"Fengle","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"635","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"issue":"3","key":"836_CR1","doi-asserted-by":"publisher","first-page":"540","DOI":"10.3390\/agriculture13030540","volume":"13","author":"M Albahar","year":"2023","unstructured":"Albahar M, 2023. A survey on deep learning and its impact on agriculture: challenges and opportunities. Agriculture, 13(3):540. https:\/\/doi.org\/10.3390\/agriculture13030540","journal-title":"Agriculture"},{"key":"836_CR2","volume-title":"Tomato, GH\/T 1193-2021","author":"All China Federation of Supply and Marketing Cooperatives","year":"2021","unstructured":"All China Federation of Supply and Marketing Cooperatives, 2021. Tomato, GH\/T 1193-2021. All China Federation of Supply and Marketing Cooperatives, China."},{"issue":"10","key":"836_CR3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1038\/s42256-020-0227-9","volume":"2","author":"S Banerjee","year":"2020","unstructured":"Banerjee S, Magee L, Wang DK, et al., 2020. Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder\u2013decoder deep networks. Nat Mach Intell, 2(10):585\u2013594. https:\/\/doi.org\/10.1038\/s42256-020-0227-9","journal-title":"Nat Mach Intell"},{"key":"836_CR4","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1145\/1102351.1102363","volume-title":"Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany","author":"C Burges","year":"2005","unstructured":"Burges C, Shaked T, Renshaw E, et al., 2005. Learning to rank using gradient descent. Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany, p.89\u201396. https:\/\/doi.org\/10.1145\/1102351.1102363"},{"key":"836_CR5","series-title":"Microsoft Research Technical Report","volume-title":"From RankNet to LambdaRank to LambdaMART: an overview","author":"CJC Burges","year":"2010","unstructured":"Burges CJC, 2010. From RankNet to LambdaRank to LambdaMART: an overview. Microsoft Research Technical Report, MSR-TR-2010-82. Available from: https:\/\/www.microsoft.com\/en-us\/research\/publication\/from-ranknet-to-lambdarank-to-lambdamart-an-overview"},{"key":"836_CR6","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.patrec.2020.11.008","volume":"140","author":"WZ Cao","year":"2020","unstructured":"Cao WZ, Mirjalili V, Raschka S, 2020. Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recogn Lett, 140:325\u2013331. https:\/\/doi.org\/10.1016\/j.patrec.2020.11.008","journal-title":"Pattern Recogn Lett"},{"key":"836_CR7","doi-asserted-by":"publisher","first-page":"108533","DOI":"10.1016\/j.compag.2023.108533","volume":"216","author":"WB Chen","year":"2024","unstructured":"Chen WB, Liu MC, Zhao CJ, et al., 2024. MTD-YOLO: multitask deep convolutional neural network for cherry tomato fruit bunch maturity detection. Comput Electron Agric, 216: 108533. https:\/\/doi.org\/10.1016\/j.compag.2023.108533","journal-title":"Comput Electron Agric"},{"key":"836_CR8","doi-asserted-by":"publisher","first-page":"108951","DOI":"10.1016\/j.compag.2024.108951","volume":"221","author":"WJ Chen","year":"2024","unstructured":"Chen WJ, Rao Y, Wang FY, et al., 2024. MLP-based multimodal tomato detection in complex scenarios: insights from task-specific analysis of feature fusion architectures. Comput Electron Agric, 221:108951. https:\/\/doi.org\/10.1016\/j.compag.2024.108951","journal-title":"Comput Electron Agric"},{"issue":"4","key":"836_CR9","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1007\/s10725-025-01320-8","volume":"105","author":"BQ Chu","year":"2025","unstructured":"Chu BQ, Guo ZY, Liu BJ, et al., 2025. Fast detection of rice striped stem borer (Chilo suppressalis) stress based on UAV sensor and multimodal segmentation method. Plant Growth Regul, 105(4):1057\u20131071. https:\/\/doi.org\/10.1007\/s10725-025-01320-8","journal-title":"Plant Growth Regul"},{"key":"836_CR10","doi-asserted-by":"publisher","unstructured":"Dao T, Fu DY, Ermon S, et al., 2022. FlashAttention: fast and memory-efficient exact attention with IO-awareness. arXiv: 2205.14135. https:\/\/doi.org\/10.48550\/arXiv.2205.14135","DOI":"10.48550\/arXiv.2205.14135"},{"key":"836_CR11","doi-asserted-by":"publisher","first-page":"4685","DOI":"10.1109\/CVPR.2019.00482","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA","author":"JK Deng","year":"2019","unstructured":"Deng JK, Guo J, Xue NN, et al., 2019. ArcFace: additive angular margin loss for deep face recognition. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA, p.4685\u20134694. https:\/\/doi.org\/10.1109\/CVPR.2019.00482"},{"key":"836_CR12","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1109\/CVPR.2019.00487","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA","author":"R D\u00edaz","year":"2019","unstructured":"D\u00edaz R, Marathe A, 2019. Soft labels for ordinal regression. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA, p.4733\u20134742. https:\/\/doi.org\/10.1109\/CVPR.2019.00487"},{"key":"836_CR13","doi-asserted-by":"publisher","first-page":"109788","DOI":"10.1016\/j.compag.2024.109788","volume":"229","author":"E Fass","year":"2025","unstructured":"Fass E, Shlomi E, Ziv C, et al., 2025. Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring. Comput Electron Agric, 229:109788. https:\/\/doi.org\/10.1016\/j.compag.2024.109788","journal-title":"Comput Electron Agric"},{"key":"836_CR14","volume-title":"Crops and livestock products","author":"Food and Agriculture Organization of the United Nations (FAO)","year":"2023","unstructured":"Food and Agriculture Organization of the United Nations (FAO), 2023. Crops and livestock products. https:\/\/www.fao.org\/faostat\/en\/#data\/QCL [Accessed on Oct. 1, 2025]."},{"key":"836_CR15","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1109\/CVPR.2018.00214","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA","author":"H Fu","year":"2018","unstructured":"Fu H, Gong MM, Wang CH, et al., 2018. Deep ordinal regression network for monocular depth estimation. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, p.2002\u20132011. https:\/\/doi.org\/10.1109\/CVPR.2018.00214"},{"issue":"6","key":"836_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1002\/jsfa.2060","volume":"85","author":"H Gautier","year":"2005","unstructured":"Gautier H, Rocci A, Buret M, et al., 2005. Fruit load or fruit position alters response to temperature and subsequently cherry tomato quality. J Sci Food Agric, 85(6): 1009\u20131016. https:\/\/doi.org\/10.1002\/jsfa.2060","journal-title":"J Sci Food Agric"},{"key":"836_CR17","doi-asserted-by":"publisher","first-page":"5900","DOI":"10.1109\/CVPR42600.2020.00594","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA","author":"YG Huang","year":"2020","unstructured":"Huang YG, Wang YH, Tai Y, et al., 2020. CurricularFace: adaptive curriculum learning loss for deep face recognition. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA, p.5900\u20135909. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00594"},{"issue":"13","key":"836_CR18","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.3390\/agriculture15131351","volume":"15","author":"Z Khan","year":"2025","unstructured":"Khan Z, Shen Y, Liu H, 2025. ObjectDetection in agriculture: a comprehensive review of methods, applications, challenges, and future directions. Agriculture, 15(13):1351. https:\/\/doi.org\/10.3390\/agriculture15131351","journal-title":"Agriculture"},{"issue":"6","key":"836_CR19","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE, 2017. ImageNet classification with deep convolutional neural networks. Commun ACM, 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"836_CR20","doi-asserted-by":"publisher","first-page":"15152","DOI":"10.1109\/CVPR52733.2024.01435","volume-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA","author":"P Kumar","year":"2024","unstructured":"Kumar P, Bhat KM, Shenvi Nadkarni VB, et al., 2024. GLiDR: topologically regularized graph generative network for sparse LiDAR point clouds. 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA, p.15152\u201315161. https:\/\/doi.org\/10.1109\/CVPR52733.2024.01435"},{"issue":"2","key":"836_CR21","doi-asserted-by":"publisher","first-page":"603","DOI":"10.3390\/agronomy13020603","volume":"13","author":"RZ Li","year":"2023","unstructured":"Li RZ, Ji ZJ, Hu SK, et al., 2023. Tomato maturity recognition model based on improved YOLOv5 in greenhouse. Agronomy, 13(2):603. https:\/\/doi.org\/10.3390\/agronomy13020603","journal-title":"Agronomy"},{"issue":"1","key":"836_CR22","doi-asserted-by":"publisher","first-page":"136","DOI":"10.11975\/j.issn.1002-6819.202210099","volume":"39","author":"XX Li","year":"2023","unstructured":"Li XX, Chen WB, Wang YQ, et al., 2023. Design and experiment of an automatic cherry tomato harvesting system based on cascade vision detection. Trans Chin Soc Agric Eng, 39(1): 136\u2013145 (in Chinese). https:\/\/doi.org\/10.11975\/j.issn.1002-6819.202210099","journal-title":"Trans Chin Soc Agric Eng"},{"issue":"12","key":"836_CR23","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.3390\/s21124115","volume":"21","author":"GS Sampaio","year":"2021","unstructured":"Sampaio GS, Silva LA, Marengoni M, 2021. 3D reconstruction of non-rigid plants and sensor data fusion for agriculture phenotyping. Sensors, 21(12):4115. https:\/\/doi.org\/10.3390\/s21124115","journal-title":"Sensors"},{"issue":"2","key":"836_CR24","doi-asserted-by":"publisher","first-page":"100014","DOI":"10.1016\/j.plaphe.2025.100014","volume":"7","author":"M Su","year":"2025","unstructured":"Su M, Zhou D, Yun YZ, et al., 2025. Design and implementation of a high-throughput field phenotyping robot for acquiring multisensor data in wheat. Plant Phenomics, 7(2): 100014. https:\/\/doi.org\/10.1016\/j.plaphe.2025.100014","journal-title":"Plant Phenomics"},{"issue":"2","key":"836_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s11263-016-0884-7","volume":"119","author":"C Vondrick","year":"2016","unstructured":"Vondrick C, Khosla A, Pirsiavash H, et al., 2016. Visualizing object detection features. Int J Comput Vision, 119(2):145\u2013158. https:\/\/doi.org\/10.1007\/s11263-016-0884-7","journal-title":"Int J Comput Vision"},{"key":"836_CR26","doi-asserted-by":"publisher","first-page":"108833","DOI":"10.1016\/j.compag.2024.108833","volume":"219","author":"AC Wang","year":"2024","unstructured":"Wang AC, Qian WH, Li A, et al., 2024. NVW-YOLOv8s: an improved YOLOv8s network for real-time detection and segmentation of tomato fruits at different ripeness stages. Comput Electron Agric, 219:108833. https:\/\/doi.org\/10.1016\/j.compag.2024.108833","journal-title":"Comput Electron Agric"},{"key":"836_CR27","doi-asserted-by":"publisher","first-page":"23217","DOI":"10.1109\/CVPR52734.2025.02162","volume-title":"2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA","author":"XR Wang","year":"2025","unstructured":"Wang XR, Guo LQ, Wang XY, et al., 2025. SoftShadow: leveraging soft masks for penumbra-aware shadow removal. 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA, p.23217\u201323226. https:\/\/doi.org\/10.1109\/CVPR52734.2025.02162"},{"key":"836_CR28","doi-asserted-by":"publisher","first-page":"101886","DOI":"10.1016\/j.ecoinf.2022.101886","volume":"72","author":"Z Wang","year":"2022","unstructured":"Wang Z, Ling YM, Wang XL, et al., 2022. An improved Faster R-CNN model for multi-object tomato maturity detection in complex scenarios. Ecol Inform, 72:101886. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101886","journal-title":"Ecol Inform"},{"issue":"6","key":"836_CR29","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.3390\/agronomy13061625","volume":"13","author":"F Xiao","year":"2023","unstructured":"Xiao F, Wang HB, Xu YQ, et al., 2023. Fruit detection and recognition based on deep learning for automatic harvesting: an overview and review. Agronomy, 13(6):1625. https:\/\/doi.org\/10.3390\/agronomy13061625","journal-title":"Agronomy"},{"key":"836_CR30","doi-asserted-by":"publisher","unstructured":"Yao J, Ke XB, Gu XY et al., 2025. Optimized substrate selection for enhanced orchid growth based on high-throughput lysimetric arrays. J Zhejiang Univ-Sci B, online first. https:\/\/doi.org\/10.1631\/jzus.B2500195","DOI":"10.1631\/jzus.B2500195"},{"issue":"6","key":"836_CR31","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1631\/jzus.B2000381","volume":"22","author":"XB Zhang","year":"2021","unstructured":"Zhang XB, Hu Y, Chen W, et al., 2021. 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 22(6):462\u2013475. https:\/\/doi.org\/10.1631\/jzus.B2000381","journal-title":"J Zhejiang Univ-Sci B (Biomed & Biotechnol)"}],"container-title":["Journal of Zhejiang University-SCIENCE B"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1631\/jzus.B2500647.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1631\/jzus.B2500647","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1631\/jzus.B2500647.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T01:00:57Z","timestamp":1779498057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1631\/jzus.B2500647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["836"],"URL":"https:\/\/doi.org\/10.1631\/jzus.b2500647","relation":{},"ISSN":["1673-1581","1862-1783"],"issn-type":[{"value":"1673-1581","type":"print"},{"value":"1862-1783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"14 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Bingquan CHU, Ruiyuan WU, Haijun ZHANG, Haochuan QIN, Zishun PENG, Fengle ZHU, and Yong HE declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethics guidelines"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethics guidelines"}}]}}