{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:31:25Z","timestamp":1783830685788,"version":"3.55.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"The Ministry of Science and Technology\u2019s National Foreign Experts Project","award":["G2022042005L"],"award-info":[{"award-number":["G2022042005L"]}]},{"name":"Gansu Province Higher Education Industry Support Project","award":["2023CYZC-54"],"award-info":[{"award-number":["2023CYZC-54"]}]},{"name":"Gansu Province Key R&D Plan","award":["23YFWA0013"],"award-info":[{"award-number":["23YFWA0013"]}]},{"DOI":"10.13039\/501100017702","name":"Lanzhou Talent Innovation and Entrepreneurship Project","doi-asserted-by":"crossref","award":["2021-RC-47"],"award-info":[{"award-number":["2021-RC-47"]}],"id":[{"id":"10.13039\/501100017702","id-type":"DOI","asserted-by":"crossref"}]},{"name":"2020 Gansu Agricultural University Graduate Education Research Project","award":["2020-19"],"award-info":[{"award-number":["2020-19"]}]},{"name":"2021 Gansu Agricultural University-level \u201cThree-dimensional Education\u201d Pilot Extension Teaching Research Project","award":["2022-9"],"award-info":[{"award-number":["2022-9"]}]},{"name":"2022 Gansu Agricultural University-level Comprehensive Professional Reform Project","award":["2021-4"],"award-info":[{"award-number":["2021-4"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s44443-025-00081-8","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:03:26Z","timestamp":1751367806000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLOv8n-DSDL: A lightweight dual-backbone network with decoupled self-attention for cotton maturity detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Meiqi","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henghui","family":"Mo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"81_CR1","doi-asserted-by":"crossref","unstructured":"Aboah A, Wang B, Bagci U, Adu-Gyamfi Y (2023) Real-time multi-class helmet violation detection using few-shot data sampling technique and yolov8. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5350\u20135358","DOI":"10.1109\/CVPRW59228.2023.00564"},{"key":"81_CR2","doi-asserted-by":"crossref","unstructured":"Al-Qizwini M, Barjasteh I, Al-Qassab H, Radha H (2017) Deep learning algorithm for autonomous driving using googlenet. In: 2017 IEEE intelligent vehicles symposium (IV), IEEE. pp. 89\u201396","DOI":"10.1109\/IVS.2017.7995703"},{"key":"81_CR3","first-page":"1","volume":"33","author":"M Aubry","year":"2014","unstructured":"Aubry M, Paris S, Hasinoff SW, Kautz J, Durand F (2014) Fast local laplacian filters: Theory and applications. ACM Trans Graphics (TOG) 33:1\u201314","journal-title":"ACM Trans Graphics (TOG)"},{"key":"81_CR4","first-page":"657","volume":"2019","author":"P Bharati","year":"2020","unstructured":"Bharati P, Pramanik A (2020) Deep learning techniques\u2014r-cnn to mask r-cnn: a survey. Comput Intell Patt Recogn: Proceedings of CIPR 2019:657\u2013668","journal-title":"Comput Intell Patt Recogn: Proceedings of CIPR"},{"key":"81_CR5","first-page":"34","volume":"4","author":"JM Bradow","year":"2000","unstructured":"Bradow JM, Davidonis GH (2000) Quantitation of fiber quality and the cotton production-processing interface: a physiologist\u2019s perspective. J Cotton Sci 4:34\u201364","journal-title":"J Cotton Sci"},{"key":"81_CR6","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"81_CR7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.biosystemseng.2017.04.009","volume":"159","author":"S C\u00e1rdenas-P\u00e9rez","year":"2017","unstructured":"C\u00e1rdenas-P\u00e9rez S, Chanona-P\u00e9rez J, M\u00e9ndez-M\u00e9ndez JV, Calder\u00f3n-Dom\u00ednguez G, L\u00f3pez-Santiago R, Perea-Flores MJ, Arzate-V\u00e1zquez I (2017) Evaluation of the ripening stages of apple (golden delicious) by means of computer vision system. Biosys Eng 159:46\u201358","journal-title":"Biosys Eng"},{"key":"81_CR8","doi-asserted-by":"crossref","unstructured":"Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE WACV, pp. 839\u2013847","DOI":"10.1109\/WACV.2018.00097"},{"key":"81_CR9","first-page":"226","volume":"13","author":"M Chen","year":"2014","unstructured":"Chen M, Dong H, Zhang J, Xu W (2014) Effects of different nitrogen applications on fiber quality formation in cotton. J Integr Agric 13:226\u2013237","journal-title":"J Integr Agric"},{"key":"81_CR10","doi-asserted-by":"crossref","unstructured":"Cheng B, Wei Y, Shi H, Feris R, Xiong J, Huang T (2018) Revisiting rcnn: On awakening the classification power of faster rcnn. In: Proceedings of the European conference on computer vision (ECCV), pp. 453\u2013468","DOI":"10.1007\/978-3-030-01267-0_28"},{"key":"81_CR11","first-page":"267","volume":"1","author":"R Chowdhury","year":"2023","unstructured":"Chowdhury R, Das A, Sagor G (2023) Characterization of tomato genotypes for important fruit quality traits. Appl Sci 1:267\u2013280","journal-title":"Appl Sci"},{"key":"81_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.14504\/ajr.8.1.3","volume":"8","author":"MS Cintron","year":"2021","unstructured":"Cintron MS, Hoven TV, Hinchliffe DJ, Hron R (2021) Examination of cotton maturity and maturity distribution using an infrared focal plane array imaging system. AATCC J Res 8:14\u201324","journal-title":"AATCC J Res"},{"key":"81_CR13","first-page":"191","volume-title":"Cotton breeding for fiber quality improvement","author":"G Constable","year":"2015","unstructured":"Constable G, Llewellyn D, Walford SA, Clement JD (2015) Cotton breeding for fiber quality improvement. Breeding for bioenergy and bioproducts, Industrial crops, pp 191\u2013232"},{"key":"81_CR14","doi-asserted-by":"crossref","unstructured":"David E, Madec S, Sadeghi-Tehran P, Aasen H, Zheng B, Liu S, Kirchgessner N, Ishikawa G, Nagasawa K, Badhon MA et\u00a0al (2020) Global wheat head detection (gwhd) dataset: A large and diverse dataset of high-resolution rgb-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics","DOI":"10.34133\/2020\/3521852"},{"key":"81_CR15","doi-asserted-by":"publisher","first-page":"608826","DOI":"10.3389\/fbioe.2021.608826","volume":"9","author":"C Felgueiras","year":"2021","unstructured":"Felgueiras C, Azoia NG, Gon\u00e7alves C, Gama M, Dourado F (2021) Trends on the cellulose-based textiles: raw materials and technologies. Front Bioeng Biotechnol 9:608826","journal-title":"Front Bioeng Biotechnol"},{"key":"81_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.fcr.2017.03.008","volume":"208","author":"L Feng","year":"2017","unstructured":"Feng L, Dai J, Tian L, Zhang H, Li W, Dong H (2017) Review of the technology for high-yielding and efficient cotton cultivation in the northwest inland cotton-growing region of china. Field Crop Res 208:18\u201326","journal-title":"Field Crop Res"},{"key":"81_CR17","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"81_CR18","unstructured":"Gower RM, Loizou N, Qian X, Sailanbayev A, Shulgin E, Richt\u00e1rik P (2019) Sgd: General analysis and improved rates. In: International conference on machine learning, PMLR. pp. 5200\u20135209"},{"key":"81_CR19","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.2134\/agronj2010.0148","volume":"102","author":"CO Gwathmey","year":"2010","unstructured":"Gwathmey CO, Tyler DD, Yin X (2010) Prospects for monitoring cotton crop maturity with normalized difference vegetation index. Agron J 102:1352\u20131360","journal-title":"Agron J"},{"key":"81_CR20","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.eaef.2018.03.001","volume":"11","author":"H Habaragamuwa","year":"2018","unstructured":"Habaragamuwa H, Ogawa Y, Suzuki T, Shiigi T, Ono M, Kondo N (2018) Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network. Eng Agricult Environ Food 11:127\u2013138","journal-title":"Eng Agricult Environ Food"},{"key":"81_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"81_CR22","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1049\/ipr2.13028","volume":"18","author":"C Hou","year":"2024","unstructured":"Hou C, Li Z, Shen X, Li G (2024) Real-time defect detection method based on yolo-gss at the edge end of a transmission line. IET Image Proc 18:1315\u20131327","journal-title":"IET Image Proc"},{"key":"81_CR23","doi-asserted-by":"publisher","first-page":"677","DOI":"10.3390\/machines11070677","volume":"11","author":"M Hussain","year":"2023","unstructured":"Hussain M (2023) Yolo-v1 to yolo-v8, the rise of yolo and its complementary nature toward digital manufacturing and industrial defect detection. Machines 11:677","journal-title":"Machines"},{"key":"81_CR24","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang P, Ergu D, Liu F, Cai Y, Ma B (2022) A review of yolo algorithm developments. Procedia Comput Sci 199:1066\u20131073","journal-title":"Procedia Comput Sci"},{"key":"81_CR25","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.3390\/agronomy13082144","volume":"13","author":"S Jiang","year":"2023","unstructured":"Jiang S, Liu Z, Hua J, Zhang Z, Zhao S, Xie F, Ao J, Wei Y, Lu J, Li Z et al (2023) A real-time detection and maturity classification method for loofah. Agronomy 13:2144","journal-title":"Agronomy"},{"key":"81_CR26","unstructured":"Jocher G, Chaurasia A, Stoken A, Borovec J, Kwon Y, Fang J, Michael K, Montes D, Nadar J, Skalski P et\u00a0al (2022) ultralytics\/yolov5: v6. 1-tensorrt, tensorflow edge tpu and openvino export and inference. Zenodo"},{"key":"81_CR27","doi-asserted-by":"crossref","unstructured":"Kollias D, Tzirakis P, Baird A, Cowen A, Zafeiriou S (2023) Abaw: Valence-arousal estimation, expression recognition, action unit detection emotional reaction intensity estimation challenges. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5889\u20135898","DOI":"10.1109\/CVPRW59228.2023.00626"},{"key":"81_CR28","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3390\/machines10030194","volume":"10","author":"I Konovalenko","year":"2022","unstructured":"Konovalenko I, Maruschak P, Kozbur H, Brezinov\u00e1 J, Brezina J, Nazarevich B, Shkira Y (2022) Influence of uneven lighting on quantitative indicators of surface defects. Machines 10:194","journal-title":"Machines"},{"key":"81_CR29","doi-asserted-by":"publisher","first-page":"107941","DOI":"10.1016\/j.knosys.2021.107941","volume":"240","author":"Y Kortli","year":"2022","unstructured":"Kortli Y, Gabsi S, Voon LFLY, Jridi M, Merzougui M, Atri M (2022) Deep embedded hybrid cnn-lstm network for lane detection on nvidia jetson xavier nx. Knowl-Based Syst 240:107941","journal-title":"Knowl-Based Syst"},{"key":"81_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10681-020-02656-0","volume":"216","author":"C Li","year":"2020","unstructured":"Li C, Fu Y, Liu Q et al (2020) A review of genetic mechanisms of early maturity in cotton (gossypium hirsutum l.). Euphytica 216:1\u201315","journal-title":"Euphytica"},{"key":"81_CR31","doi-asserted-by":"publisher","first-page":"4845","DOI":"10.3390\/s21144845","volume":"21","author":"J Li","year":"2021","unstructured":"Li J, Li C, Fei S, Ma C, Chen W, Ding F, Wang Y, Li Y, Shi J, Xiao Z (2021) Wheat ear recognition based on retinanet and transfer learning. Sensors 21:4845","journal-title":"Sensors"},{"key":"81_CR32","doi-asserted-by":"crossref","unstructured":"Li X, Cui G, Lin T (2023) Long staple cotton detection method based on improved swin transformer model. In: 2023 38th Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE. pp. 347\u2013352","DOI":"10.1109\/YAC59482.2023.10401423"},{"key":"81_CR33","doi-asserted-by":"crossref","unstructured":"Liang Z, Cui G, Xiong M, Li X, Jin X, Lin T (2023) Yolo-c: An efficient and robust detection algorithm for mature long staple cotton targets with high-resolution rgb images. Agronomy 13","DOI":"10.3390\/agronomy13081988"},{"key":"81_CR34","doi-asserted-by":"publisher","first-page":"107576","DOI":"10.1016\/j.compag.2022.107576","volume":"204","author":"Q Liu","year":"2023","unstructured":"Liu Q, Zhang Y, Yang G (2023) Small unopened cotton boll counting by detection with mrf-yolo in the wild. Comput Electron Agric 204:107576","journal-title":"Comput Electron Agric"},{"key":"81_CR35","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, Springer. pp. 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"81_CR36","doi-asserted-by":"crossref","unstructured":"Liu W, Lu H, Fu H, Cao Z (2023b) Learning to upsample by learning to sample. In: Proceedings of the IEEE\/CVF, pp. 6027\u20136037","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"81_CR37","doi-asserted-by":"crossref","unstructured":"Liu Z, Xiao D (2020) Recognition method of mature strawberry based on improved ssd deep convolution neural network. In: Bio-inspired Computing: Theories and Applications: 14th International Conference, BIC-TA 2019, Zhengzhou, China, November 22\u201325, 2019, Revised Selected Papers, Part II 14, Springer. pp. 271\u2013281","DOI":"10.1007\/978-981-15-3415-7_22"},{"key":"81_CR38","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.1109\/TPAMI.2024.3350004","volume":"46","author":"A Melnik","year":"2024","unstructured":"Melnik A, Miasayedzenkau M, Makaravets D, Pirshtuk D, Akbulut E, Holzmann D, Renusch T, Reichert G, Ritter H (2024) Face generation and editing with stylegan: A survey. IEEE Trans Pattern Anal Mach Intell 46:3557\u20133576","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"81_CR39","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.scienta.2018.03.057","volume":"237","author":"FS Mim","year":"2018","unstructured":"Mim FS, Galib SM, Hasan MF, Jerin SA (2018) Automatic detection of mango ripening stages-an application of information technology to botany. Sci Hortic 237:156\u2013163","journal-title":"Sci Hortic"},{"key":"81_CR40","doi-asserted-by":"publisher","first-page":"102133","DOI":"10.1016\/j.jksuci.2024.102133","volume":"36","author":"H Mo","year":"2024","unstructured":"Mo H, Wei L (2024) Lightweight citrus leaf disease detection model based on arms and cross-domain dynamic attention. J King Saud Univ-Comput Inf Sci 36:102133","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"81_CR41","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.scienta.2014.12.037","volume":"184","author":"V Mohammadi","year":"2015","unstructured":"Mohammadi V, Kheiralipour K, Ghasemi-Varnamkhasti M (2015) Detecting maturity of persimmon fruit based on image processing technique. Sci Hortic 184:123\u2013128","journal-title":"Sci Hortic"},{"key":"81_CR42","doi-asserted-by":"crossref","unstructured":"Nasiri A, Taheri-Garavand A, Zhang Y (2019) Image-based deep learning automated sorting of date fruit. Postharvest Biol Technol 153:133\u2013141","DOI":"10.1016\/j.postharvbio.2019.04.003"},{"key":"81_CR43","doi-asserted-by":"publisher","first-page":"7729","DOI":"10.3168\/jds.2014-8123","volume":"97","author":"M Palmonari","year":"2014","unstructured":"Palmonari M, Fustini M, Canestrari G et al (2014) Influence of maturity on alfalfa hay nutritional fractions and indigestible fiber content. J Dairy Sci 97:7729\u20137734","journal-title":"J Dairy Sci"},{"key":"81_CR44","doi-asserted-by":"publisher","first-page":"108762","DOI":"10.1016\/j.fcr.2022.108762","volume":"290","author":"BR Sapkota","year":"2023","unstructured":"Sapkota BR, Adams CB, Kelly B, Rajan N, Ale S (2023) Plant population density in cotton: Addressing knowledge gaps in stand uniformity and lint quality under dryland and irrigated conditions. Field Crop Res 290:108762","journal-title":"Field Crop Res"},{"key":"81_CR45","first-page":"142","volume":"5","author":"N Singh","year":"2021","unstructured":"Singh N, Tewari V, Biswas P, Pareek C, Dhruw L (2021) Image processing algorithms for in-field cotton boll detection in natural lighting conditions. Artif Intell Agricult 5:142\u2013156","journal-title":"Artif Intell Agricult"},{"key":"81_CR46","doi-asserted-by":"publisher","first-page":"100130","DOI":"10.1016\/j.atech.2022.100130","volume":"3","author":"P Siricharoen","year":"2023","unstructured":"Siricharoen P, Yomsatieankul W, Bunsri T (2023) Fruit maturity grading framework for small dataset using single image multi-object sampling and mask r-cnn. Smart Agricult Technol 3:100130","journal-title":"Smart Agricult Technol"},{"key":"81_CR47","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1007\/s13197-013-1188-3","volume":"52","author":"D Surya Prabha","year":"2015","unstructured":"Surya Prabha D, Satheesh Kumar J (2015) Assessment of banana fruit maturity by image processing technique. J Food Sci Technol 52:1316\u20131327","journal-title":"J Food Sci Technol"},{"key":"81_CR48","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"81_CR49","doi-asserted-by":"publisher","first-page":"105307","DOI":"10.1016\/j.compag.2020.105307","volume":"171","author":"D Tedesco-Oliveira","year":"2020","unstructured":"Tedesco-Oliveira D, da Silva RP, Maldonado W Jr, Zerbato C (2020) Convolutional neural networks in predicting cotton yield from images of commercial fields. Comput Electron Agric 171:105307","journal-title":"Comput Electron Agric"},{"key":"81_CR50","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.compag.2018.01.011","volume":"146","author":"P Wan","year":"2018","unstructured":"Wan P, Toudeshki A, Tan H, Ehsani R (2018) A methodology for fresh tomato maturity detection using computer vision. Comput Electron Agric 146:43\u201350","journal-title":"Comput Electron Agric"},{"key":"81_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10681-020-02567-0","volume":"216","author":"Y Wang","year":"2020","unstructured":"Wang Y, Chen M (2020) A review of genetic mechanisms of early maturity in cotton (gossypium hirsutum l.). Euphytica 216:1\u201315. https:\/\/doi.org\/10.1007\/s10681-020-02567-0","journal-title":"Euphytica"},{"key":"81_CR52","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/rs11050531","volume":"11","author":"Y Wang","year":"2019","unstructured":"Wang Y, Wang C, Zhang H, Dong Y, Wei S (2019) Automatic ship detection based on retinanet using multi-resolution gaofen-3 imagery. Remote Sensing 11:531","journal-title":"Remote Sensing"},{"key":"81_CR53","doi-asserted-by":"publisher","first-page":"041002","DOI":"10.1115\/1.4063860","volume":"24","author":"Y Wang","year":"2024","unstructured":"Wang Y, Wang X, Hao R, Lu B, Huang B (2024) Metal surface defect detection method based on improved cascade r-cnn. J Comput Inf Sci Eng 24:041002","journal-title":"J Comput Inf Sci Eng"},{"key":"81_CR54","first-page":"68","volume":"234","author":"G Xiao","year":"2019","unstructured":"Xiao G, Zhao P, Zhang P (2019) Responses of cotton yield and fiber quality to water deficit stress during different growth stages. Field Crop Res 234:68\u201376","journal-title":"Field Crop Res"},{"key":"81_CR55","unstructured":"Yehudai G, Shamir O (2019) On the power and limitations of random features for understanding neural networks. Adv Neural Inf Process Syst 32"},{"key":"81_CR56","doi-asserted-by":"crossref","unstructured":"Yiming C, Guohao F, Chunchang Z (2024) An efficient and lightweight yolov8s strawberry maturity detection model. J Agricult Sci Technol 46\u201366","DOI":"10.17265\/2161-6256\/2024.02.002"},{"key":"81_CR57","doi-asserted-by":"publisher","first-page":"12274","DOI":"10.3390\/su141912274","volume":"14","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Guo Z, Wu J, Tian Y, Tang H, Guo X (2022) Real-time vehicle detection based on improved yolo v5. Sustainability 14:12274","journal-title":"Sustainability"},{"key":"81_CR58","first-page":"2193","volume":"42","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Yang G, Liu Y, Wang C, Yin Y (2022) An improved yolo network for unopened cotton boll detection in the field. J Intell Fuzzy Syst 42:2193\u20132206","journal-title":"J Intell Fuzzy Syst"},{"key":"81_CR59","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1111\/nph.18611","volume":"237","author":"H Zhao","year":"2023","unstructured":"Zhao H, Chen Y, Liu J, Wang Z, Li F, Ge X (2023) Recent advances and future perspectives in early-maturing cotton research. New Phytol 237:1100\u20131114","journal-title":"New Phytol"},{"key":"81_CR60","doi-asserted-by":"publisher","first-page":"112607","DOI":"10.1016\/j.scienta.2023.112607","volume":"324","author":"X Zhu","year":"2024","unstructured":"Zhu X, Chen F, Zhang X, Zheng Y, Peng X, Chen C (2024) Detection the maturity of multi-cultivar olive fruit in orchard environments based on olive-efficientdet. Sci Hortic 324:112607","journal-title":"Sci Hortic"},{"key":"81_CR61","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00081-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00081-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00081-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T00:19:28Z","timestamp":1757204368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00081-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":61,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["81"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00081-8","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"13 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"The Ministry of Science and Technology\u2019s National Foreign Experts Project (G2022042005L); Gansu Province Higher Education Industry Support Project (2023CYZC-54); Gansu Province Key R&D Plan (23YFWA0013); Lanzhou Talent Innovation and Entrepreneurship Project (2021-RC-47); 2020 Gansu Agricultural University Graduate Education Research Project (2020-19); 2021 Gansu Agricultural University-level \u201cThree-dimensional Education\u201d Pilot Extension Teaching Research Project (2022-9); 2022 Gansu Agricultural Universitylevel Comprehensive Professional Reform Project (2021-4).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Founding source"}}],"article-number":"88"}}