{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:04:45Z","timestamp":1791230685305,"version":"4.1.0"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008871","name":"Yunnan Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["202302AO370003, 202401AT070103, 202501AT070008"],"award-info":[{"award-number":["202302AO370003, 202401AT070103, 202501AT070008"]}],"id":[{"id":"10.13039\/501100008871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>In complex greenhouse environments, black raspberries present challenges such as dense clusters, variable fruit sizes, continuous colour changes during ripening, and interference from foliage and uneven lighting. These issues lead to high false positives and false negatives in existing ripeness detection models, limiting their application in intelligent precision harvesting. To address this, this study proposes SDGD-YOLO, a novel ripeness detection model. First, a StarNet architecture is embedded into the feature extraction network to enhance cross-channel interaction and global semantic association, improving the extraction of key ripeness features. Second, a DySample dynamic upsampling module is introduced during multi-scale feature fusion to adapt to variations in fruit size and morphology across ripeness stages. Third, a collaborative optimization mechanism combining Soft-NMS and GIoU is applied in post-processing to reduce redundant bounding boxes and improve localization accuracy, effectively mitigating false positives and negatives under occlusion. Finally, a refined knowledge distillation mechanism via teacher-student joint training enhances detection performance while maintaining lightweight inference. The experimental results show that, compared with the baseline YOLOv13n, SDGD-YOLO achieves a precision of 91.0% (an increase of 3.6%), a recall of 71.8% (an increase of 4.7%), and an F1-score of 80.68% (an increase of 4.69%). with mAP50 and mAP50\u201395 reaching 81.7% (an increase of 5.7%) and 68.7% (an increase of 6.6%) respectively. Meanwhile, the number of model parameters has been reduced to 1.988 M, and GFLOPs to 5.8 G, achieving an effective balance between improved accuracy and reduced computational complexity. The SDGD-YOLO proposed in this study provides a technical reference for the accurate detection of berry ripeness in complex greenhouse environments.<\/jats:p>","DOI":"10.3389\/fpls.2026.1843631","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:43:40Z","timestamp":1778823820000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["SDGD-YOLO: a lightweight detection model for assessing the ripeness of black raspberries in complex greenhouse environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Wenyan","family":"Zhao","sequence":"first","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University","place":["Kunming, China"]},{"name":"Key Laboratory of Resources and Environmental Remote Sensing, Yunnan Provincial Universities","place":["Kunming, China"]},{"name":"Yunnan Provincial Engineering Research Centre for Geospatial Information Technology","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Ding","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University","place":["Kunming, China"]},{"name":"Key Laboratory of Resources and Environmental Remote Sensing, Yunnan Provincial Universities","place":["Kunming, China"]},{"name":"Yunnan Provincial Engineering Research Centre for Geospatial Information Technology","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University","place":["Kunming, China"]},{"name":"Key Laboratory of Resources and Environmental Remote Sensing, Yunnan Provincial Universities","place":["Kunming, China"]},{"name":"Yunnan Provincial Engineering Research Centre for Geospatial Information Technology","place":["Kunming, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"B1","article-title":"Efficient non-maximum suppression","volume-title":"Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06)","author":"Alexander","year":"2006"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s43014-023-00205-5","article-title":"State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions","volume":"6","author":"Anjali","year":"2024","journal-title":"Food Prod. Process. Nutr."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy14050910","article-title":"Detection of Gannan navel orange ripeness in natural environment based on YOLOv5-NMM","volume":"14","author":"Binbin","year":"2024","journal-title":"Agronomy"},{"key":"B4","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.biosystemseng.2021.10.009","article-title":"A convolutional neural network approach to detecting fruit physiological disorders and maturity in \u2018Abb\u00e9 F\u00e9tel\u2019 pears","volume":"212","author":"Bonora","year":"2021","journal-title":"Biosyst. Eng."},{"key":"B5","article-title":"Decoupled knowledge distillation","author":"Borui","year":"2022"},{"key":"B6","article-title":"Channel-wise knowledge distillation for dense prediction","author":"Changyong","year":"2021"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/agronomy14040658","article-title":"YOLO-BLBE: A novel model for identifying blueberry fruits with different maturities using the I-MSRCR method","volume":"14","author":"Chenglin","year":"2024","journal-title":"Agronomy"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/agriculture14081346","article-title":"A review of perception technologies for berry fruit-picking robots: Advantages, disadvantages, challenges, and prospects","volume":"14","author":"Chenglin","year":"2024","journal-title":"Agriculture"},{"key":"B9","article-title":"MobileNetV4: Universal models for the mobile ecosystem","volume-title":"arXiv:2404.10518v2 [cs.CV]","author":"Danfeng","year":"2024"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1503.02531","article-title":"Distilling the knowledge in a neural network","author":"Geoffrey","year":"2015","journal-title":"arXiv"},{"key":"B11","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.aiia.2024.06.004","article-title":"Computer vision in smart agriculture and precision farming: Techniques and applications","volume":"13","author":"Ghazal","year":"2024","journal-title":"Artif. Intell. Agric."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3934\/agrfood.2025001","article-title":"Appropriate harvest maturity for exploitation of wild black raspberry (Rubus sp.) fruits during shelf life period from Rawalakot, Azad Jammu and Kashmir","volume":"10","author":"Hayyat","year":"2025","journal-title":"AIMS Agric. Food"},{"key":"B13","article-title":"Maturity analysis of citrus based on machine vision and android mobile platform technology","volume-title":"2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)","author":"Hongxing","year":"2018"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.55041\/ijsrem29725","article-title":"Advancements in precision agriculture: Integrating computer vision for intelligent soil and crop monitoring in the era of artificial intelligence","volume":"8","author":"Jeffrey","year":"2024","journal-title":"Int. J. Sci. Res. Eng. Manage."},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2025.122419","article-title":"DPDB-YOLO: A lightweight YOLOv13 cherry tomato ripeness detection method with adaptive extraction module and multi-scale feature fusion architecture","volume":"238","author":"Jia","year":"2025","journal-title":"Ind. Crops Prod."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/agronomy14122783","article-title":"State-of-the-art techniques for fruit maturity detection","volume":"14","author":"Jie","year":"2024","journal-title":"Agronomy"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1908.11069","article-title":"StarNet: Targeted computation for object detection in point clouds","author":"Jiquan","year":"2019","journal-title":"arXiv"},{"key":"B18","article-title":"GhostNet: More features from cheap operations","author":"Kai","year":"2020"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s11119-019-09642-0","article-title":"Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO\u2019","volume":"20","author":"Koirala","year":"2019","journal-title":"Precis. Agric."},{"key":"B20","article-title":"Exploring inter-channel correlation for diversity-preserved knowledge distillation","author":"Li","year":"2023"},{"key":"B21","doi-asserted-by":"publisher","first-page":"101513","DOI":"10.1016\/j.atech.2025.101513","article-title":"CMD-YOLO: A lightweight model for cherry maturity detection targeting small object","volume":"9","author":"Li","year":"2025","journal-title":"Smart Agricultural Technology"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compag.2024.109118","article-title":"Faster-YOLO-AP: A lightweight apple detection algorithm based on improved YOLOv8 with a new efficient PDWConv in orchard","volume":"223","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"B23","article-title":"Bridging cross-task protocol inconsistency for distillation in dense object detection","author":"Longrong","year":"2023"},{"key":"B24","article-title":"YOLOv13: Real-time object detection with hypergraph-enhanced adaptive visual perception","volume-title":"arXiv preprint","author":"Mengqi","year":"2025"},{"key":"B25","doi-asserted-by":"publisher","first-page":"5561","DOI":"10.1109\/ICCV.2017.593","article-title":"Soft-NMS \u2013 Improving object detection with one line of code","volume":"2017","author":"Navaneeth","year":"2017","journal-title":"IEEE Int. Conf. Comput. Vision (ICCV)"},{"key":"B26","article-title":"ShuffleNet V2: Practical guidelines for efficient CNN architecture design","author":"Ningning","year":"2018"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11130-025-01373-0","article-title":"Changes in bioactive and physicochemical composition of Rubus during three developmental stages","volume":"80","author":"Nisar","year":"2025","journal-title":"Plant Foods Hum. Nutr."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/ijdats.2023.10055387","article-title":"Improving the accuracy of real field pomegranate fruit diseases detection and visualization using convolution neural networks and Grad-CAM","volume":"15","author":"Rathi","year":"2023","journal-title":"Int. J. Data Anal. Techniques Strategies"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1109\/ICCAE56788.2023.10111204","article-title":"Determination of sugar apple ripeness via image processing using convolutional neural network","author":"Sanchez","year":"2023"},{"key":"B30","doi-asserted-by":"publisher","first-page":"34","DOI":"10.54254\/2753-8818\/2025.Ch22224","article-title":"A review of computer vision technologies in precision agriculture","volume":"101","author":"Wang","year":"2025","journal-title":"Theor. Natural Sci."},{"key":"B31","doi-asserted-by":"crossref","DOI":"10.1109\/ICIP49359.2023.10222195","article-title":"BCKD: Block-correlation knowledge distillation","author":"Wang","year":"2023"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCV51070.2023.00554","article-title":"Learning to upsample by learning to sample","author":"Wenze","year":"2023","journal-title":"IEEE Int. Conf. Comput. Vision(ICCV)"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14010036","article-title":"A lightweight detection method for blueberry fruit maturity based on an improved YOLOv5 algorithm","volume":"14","author":"Xiao","year":"2023","journal-title":"Agriculture"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CVPR52733.2024.00544","article-title":"Rewrite the stars","author":"Xu","year":"2024","journal-title":"IEEE\/CVF Conf. Comput. Vision Pattern Recognition(CVPR)"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1395796","article-title":"A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on YOLOv8s","volume":"15","author":"Yang","year":"2024","journal-title":"Front. Plant Sci."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compag.2023.108360","article-title":"Strawberry ripeness detection based on YOLOv8 algorithm fused with LW-Swin Transformer","volume":"215","author":"Yang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.postharvbio.2024.113205","article-title":"Methyl jasmonate and salicylic acid treatment changes the nutritional quality, antioxidant profile and gene expression of postharvest blackberry fruit","volume":"219","author":"Yaqiong","year":"2025","journal-title":"Postharvest Biol. Technol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.fbio.2024.105371","article-title":"Research progress on utilization and molecular biology of blackberry","volume":"62","author":"Yaqiong","year":"2024","journal-title":"Food Biosci."},{"key":"B39","article-title":"Distance-IoU loss: Faster and better learning for bounding box regression","author":"Zhaohui","year":"2020"},{"key":"B40","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s12393-023-09353-3","article-title":"The application of optical non-destructive testing for fresh berry fruits","volume":"16","author":"Zhujun","year":"2023","journal-title":"Food Eng. Rev."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1843631\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:43:44Z","timestamp":1778823824000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1843631\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":40,"alternative-id":["10.3389\/fpls.2026.1843631"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1843631","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]},"article-number":"1843631"}}