{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:48:35Z","timestamp":1782391715770,"version":"3.54.5"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research and Development Joint Fund","doi-asserted-by":"publisher","award":["225101610053"],"award-info":[{"award-number":["225101610053"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3568847","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T17:45:14Z","timestamp":1747071914000},"page":"83944-83955","source":"Crossref","is-referenced-by-count":5,"title":["Improved YOLOv8 Algorithm was Used to Segment Cucumber Seedlings Under Complex Artificial Light Conditions"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8643-828X","authenticated-orcid":false,"given":"Duokuo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingfu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7483-4034","authenticated-orcid":false,"given":"Kun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Zhoukou Normal University, Zhoukou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202105009"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodres.2021.110811","article-title":"Consumer attitudes to vertical farming (indoor plant factory with artificial lighting) in China, Singapore, U.K., and USA: A multi-method study","volume":"150","author":"Ares","year":"2021","journal-title":"Food Res. Int."},{"key":"ref3","first-page":"89","article-title":"Cucumber","author":"Mariod","journal-title":"Unconventional Oilseeds and Oil Sources"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.coisb.2017.07.002","article-title":"Unlocking the potential of plant phenotyping data through integration and data-driven approaches","volume":"4","author":"Coppens","year":"2017","journal-title":"Current Opinion Syst. Biol."},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.geomorph.2020.107045","article-title":"Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery","volume":"354","author":"Li","year":"2020","journal-title":"Geomorphology"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112818","article-title":"Integrating topographic knowledge into deep learning for the void-filling of digital elevation models","volume":"269","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"issue":"22","key":"ref7","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.3390\/plants13223217","article-title":"Multimodal data fusion for precise lettuce phenotype estimation using deep learning algorithms","volume":"13","author":"Hou","year":"2024","journal-title":"Plants"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106128","article-title":"UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation","volume":"184","author":"Chivasa","year":"2021","journal-title":"Comput. Electron. Agricult."},{"key":"ref9","article-title":"A segmentation-guided deep learning framework for leaf counting","volume":"13","author":"Fan","year":"2022","journal-title":"Frontiers Plant Sci."},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101583","article-title":"Eff-UNet++: A novel architecture for plant leaf segmentation and counting","volume":"68","author":"Bhagat","year":"2022","journal-title":"Ecological Informat."},{"issue":"2","key":"ref11","first-page":"195","article-title":"Application status and challenges of machine vision in plant factory-a review","volume":"9","author":"Tian","year":"2022","journal-title":"Inf. Process. Agricult."},{"issue":"4","key":"ref12","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.11591\/eei.v9i4.2353","article-title":"Fruit sorting robot based on color and size for an agricultural product packaging system","volume":"9","author":"Dewi","year":"2020","journal-title":"Bull. Electr. Eng. Informat."},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-017-9503-z"},{"key":"ref14","article-title":"Leaf counting from uncontrolled acquired images from greenhouse workers","volume-title":"Proc. Comput. Vis. Problems Plant Phenotyping (CVPPP)","author":"Valente"},{"key":"ref15","article-title":"AI based Rice leaf disease identification enhanced by dynamic mode decomposition","volume":"120","author":"Sudhesh","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"5","key":"ref16","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/j.cj.2023.04.014","article-title":"High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing","volume":"11","author":"Zhang","year":"2023","journal-title":"Crop J."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.34133\/2019\/7507131"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105753","article-title":"Automatic segmentation of overlapped poplar seedling leaves combining mask R-CNN and DBSCAN","volume":"178","author":"Liu","year":"2020","journal-title":"Comput. Electron. Agricult."},{"issue":"11","key":"ref19","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.3390\/agronomy14112664","article-title":"Cucumber leaf segmentation based on bilayer convolutional network","volume":"14","author":"Qian","year":"2024","journal-title":"Agronomy"},{"issue":"6","key":"ref20","first-page":"179","article-title":"Research and application of machine vision in plant growth monitoring","volume":"47","author":"Yang","year":"2019","journal-title":"Jiangsu Agricult. Sci."},{"key":"ref21","article-title":"Research on transplanting robot in facility agriculture based on machine vision","author":"Ren","year":"2007"},{"key":"ref22","article-title":"Design and implementation of plant factory control system based on visual inspection robot","author":"He","year":"2019"},{"issue":"4","key":"ref23","doi-asserted-by":"crossref","first-page":"888","DOI":"10.3390\/agriculture13040888","article-title":"Artificial neural network-based seedling phenotypic information acquisition of plant factory","volume":"13","author":"Chen","year":"2023","journal-title":"Agriculture"},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2019.00227","article-title":"Leaf-Movement-Based growth prediction model using optical flow analysis and machine learning in plant factory","volume":"10","author":"Nagano","year":"2019","journal-title":"Frontiers Plant Sci."},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106714","article-title":"EPSA-YOLO-v5s: A novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms","volume":"193","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agricult."},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2024.1365266","article-title":"Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory","volume":"15","author":"Kim","year":"2024","journal-title":"Frontiers Plant Sci."},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-34372-9","volume-title":"Computer Vision: Algorithms and Applications","author":"Szeliski","year":"2022"},{"issue":"1","key":"ref28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst., Man, Cybern."},{"key":"ref29","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","article-title":"Image segmentation using k -means clustering algorithm and subtractive clustering algorithm","volume":"54","author":"Dhanachandra","year":"2015","journal-title":"Proc. Comput. Sci."},{"issue":"11","key":"ref30","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2004.110","article-title":"Statistical region merging","volume":"26","author":"Nock","year":"2004","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"ref31","volume-title":"Image Object Detection Method Based on Improved Faster R-CNN","author":"Yin","year":"2023"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/bf00994018"},{"issue":"8","key":"ref33","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"ref34","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref35","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2014","journal-title":"arXiv:1411.4038"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref38","article-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2015","journal-title":"arXiv:1506.02640"},{"key":"ref39","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref40","volume-title":"Ultralytics\/YOLOv5: V7.0\u2014YOLOv5 SOTA Realtime Instance Segmentation","author":"Jocher et al","year":"2022"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref42","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108623","article-title":"FRPNet: An improved faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene","volume":"217","author":"Yang","year":"2024","journal-title":"Comput. Electron. Agricult."},{"key":"ref43","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2016","journal-title":"arXiv:1610.02357"},{"key":"ref44","article-title":"Feature pyramid networks for object detection","author":"Lin","year":"2016","journal-title":"arXiv:1612.03144"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00929"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref47","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2017","journal-title":"arXiv:1709.01507"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref49","first-page":"423","volume-title":"MarineYOLO: Innovative Deep Learning Method for Small Target Detection in Underwater Environments","volume":"104","author":"Liu"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00147"},{"key":"ref51","article-title":"Deep residual learning for image recognition","author":"He","year":"2015","journal-title":"arXiv:1512.03385"},{"key":"ref52","article-title":"SOLOv2: Dynamic and fast instance segmentation","author":"Wang","year":"2020","journal-title":"arXiv:2003.10152"},{"key":"ref53","article-title":"PointRend: Image segmentation as rendering","author":"Kirillov","year":"2019","journal-title":"arXiv:1912.08193"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref55","article-title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2022","journal-title":"arXiv:2207.02696"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11000110.pdf?arnumber=11000110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T17:58:54Z","timestamp":1747677534000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11000110\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3568847","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}