{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:24:46Z","timestamp":1723335886338},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1016\/j.compag.2024.109213","type":"journal-article","created":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T05:41:03Z","timestamp":1720935663000},"page":"109213","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An automatic tracking method for fruit abscission of litchi using convolutional networks"],"prefix":"10.1016","volume":"224","author":[{"given":"Tong","family":"Huang","sequence":"first","affiliation":[]},{"given":"Jingfeng","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Long","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Houbin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zuanxian","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7620-1018","authenticated-orcid":false,"given":"Yueju","family":"Xue","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2024.109213_b0010","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2020.571299","article-title":"Tomato fruit detection and counting in greenhouses using deep learning","volume":"11","author":"Afonso","year":"2020","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2024.109213_b0015","article-title":"Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity","volume":"169","author":"Ali","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.compag.2024.109213_b0020","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s43670-022-00029-3","article-title":"Homogeneous vector bundles and G-equivariant convolutional neural networks","volume":"20","author":"Aronsson","year":"2022","journal-title":"Sampling Theory, Signal Processing, and Data Analysis"},{"key":"10.1016\/j.compag.2024.109213_b0025","series-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"9252","article-title":"An unsupervised learning model for deformable medical image registration","author":"Balakrishnan","year":"2018"},{"key":"10.1016\/j.compag.2024.109213_b0030","series-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5110","article-title":"A case for using rotation invariant features in state of the art feature matchers","author":"B\u00f6kman","year":"2022"},{"issue":"1","key":"10.1016\/j.compag.2024.109213_b0035","first-page":"6","article-title":"Research progress on litchi fruit abscission","volume":"29","author":"Cai","year":"2017","journal-title":"Jiangxi Agri. J."},{"issue":"5","key":"10.1016\/j.compag.2024.109213_b0040","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s11119-022-09895-2","article-title":"Citrus fruits maturity detection in natural environments based on convolutional neural networks and visual saliency map","volume":"23","author":"Chen","year":"2022","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2024.109213_b0045","unstructured":"Cohen, T., Weiler, M., Kicanaoglu, B., & Welling, M. (2019). Gauge equivariant convolutional networks and the icosahedral CNN. In International conference on Machine learning (pp. 1321-1330). PMLR. Doi: 10.48550\/arXiv.1902.04615."},{"issue":"8","key":"10.1016\/j.compag.2024.109213_b0050","first-page":"47","article-title":"Improved algorithm for fast image matching based on SURF","volume":"43","author":"Cui","year":"2023","journal-title":"J. Instrum."},{"key":"10.1016\/j.compag.2024.109213_b0055","doi-asserted-by":"crossref","unstructured":"Das, P., & Yadav, J. (2020). Automated tomato maturity grading system using CNN. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 136-142). IEEE. Doi: 10.1109\/icosec49089.2020.9215451.","DOI":"10.1109\/ICOSEC49089.2020.9215451"},{"key":"10.1016\/j.compag.2024.109213_b0060","series-title":"In the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","article-title":"Self-supervised interest point detection and description\u2019","author":"DeTone","year":"2018"},{"key":"10.1016\/j.compag.2024.109213_b0070","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.plantsci.2012.10.008","article-title":"Elucidating mechanisms underlying organ abscission","volume":"199","author":"Estornell","year":"2013","journal-title":"Plant Sci."},{"key":"10.1016\/j.compag.2024.109213_b0075","first-page":"5","article-title":"A study on the botanical and fruiting characteristics of a new lychee variety, Lingfengnuo","volume":"1","author":"Fan","year":"2015","journal-title":"Chinese Fruit Trees"},{"key":"10.1016\/j.compag.2024.109213_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105634","article-title":"Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN","volume":"176","author":"Gao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2024.109213_b0085","series-title":"In Proceedings of the IEEE International Conference on Computer Vision","first-page":"2961","article-title":"Mask r-cnn","author":"He","year":"2017"},{"key":"10.1016\/j.compag.2024.109213_b0090","doi-asserted-by":"crossref","unstructured":"Hoffmann, S., Brust, C., Shadaydeh, M., & Denzler, J. (2019). Registration of high resolution SAR and optical satellite imagery using fully convolutional networks. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 5152-5155). IEEE. Doi: 10.1109\/IGARSS.2019.8898714.","DOI":"10.1109\/IGARSS.2019.8898714"},{"key":"10.1016\/j.compag.2024.109213_b0095","first-page":"1673","article-title":"The effect of thinning treatment on the flowering and fruiting of late maturing litchi","volume":"4","author":"Hu","year":"2020","journal-title":"Tropical Agriculture in China"},{"key":"10.1016\/j.compag.2024.109213_b0100","unstructured":"Jocher, G., Stoken, A., Borovec, J., Chaurasia, A., Changyu, L., Hogan, A., & Ingham, F. (2021). YOLOv5. Retrieved January 15, 2021, from https:\/\/ github. com\/ ultralytics\/ yoloV5."},{"key":"10.1016\/j.compag.2024.109213_b0105","unstructured":"L. Lang, M. Weiler A wigner-eckart theorem for group equivariant convolution kernels. arXiv preprint arXiv:2010.10952 2020 10.48550\/arXiv.2010.10952."},{"key":"10.1016\/j.compag.2024.109213_b0110","series-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13955","article-title":"Training a Steerable CNN for Guidewire Detection","author":"Li","year":"2020"},{"issue":"5","key":"10.1016\/j.compag.2024.109213_b0115","first-page":"119","article-title":"Research progress in the physiology and molecular biology of lychee flower and fruit development","volume":"40","author":"Li","year":"2019","journal-title":"Journal of South China Agricultural University"},{"key":"10.1016\/j.compag.2024.109213_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105192","article-title":"A visual detection method for nighttime litchi fruits and fruiting stems","volume":"169","author":"Liang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2024.109213_b0125","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (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 (pp. 21-37). Doi: 10.1007\/978-3-319-46448-0_2.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"7","key":"10.1016\/j.compag.2024.109213_b0130","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.3390\/s20072145","article-title":"YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3","volume":"20","author":"Liu","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2024.109213_b0135","series-title":"In Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"168","article-title":"Geodesc: Learning local descriptors by integrating geometry constraints","author":"Luo","year":"2018"},{"key":"10.1016\/j.compag.2024.109213_b0140","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image matching from handcrafted to deep features: A survey","volume":"129","author":"Ma","year":"2021","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"10.1016\/j.compag.2024.109213_b0145","first-page":"1007","article-title":"Comparison of the flowering and fruiting characteristics of \u201cIce Litchi\u201d and \u201cNuomici\u201d litchi","volume":"50","author":"Ma","year":"2021","journal-title":"Fruit Trees in Southern China"},{"issue":"10","key":"10.1016\/j.compag.2024.109213_b0150","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/TPAMI.2005.188","article-title":"A performance evaluation oflocal descriptors","volume":"27","author":"Mikolajczyk","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compag.2024.109213_b0155","first-page":"132","article-title":"Development status, trend and suggestion of litchi industry in mainland China","volume":"46","author":"Qi","year":"2019","journal-title":"Guangdong Agric. Sci"},{"key":"10.1016\/j.compag.2024.109213_b0160","unstructured":"Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. Doi: 10.48550\/arXiv.1804.02767."},{"issue":"6","key":"10.1016\/j.compag.2024.109213_b0165","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compag.2024.109213_b0170","series-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4938","article-title":"Superglue: Learning feature matching with graph neural networks","author":"Sarlin","year":"2020"},{"key":"10.1016\/j.compag.2024.109213_b0175","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE","article-title":"Quad-networks: unsupervised learning to rank for interest point detection","author":"Savinov","year":"2017"},{"key":"10.1016\/j.compag.2024.109213_b0180","series-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1482","article-title":"Comparative evaluation of hand-crafted and learned local features","author":"Schonberger","year":"2017"},{"key":"10.1016\/j.compag.2024.109213_b0190","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. Doi: 10.48550\/arXiv.1409.1556."},{"issue":"5","key":"10.1016\/j.compag.2024.109213_b0195","first-page":"5","article-title":"Improved UAV image matching algorithm based on sift","volume":"30","author":"Sun","year":"2023","journal-title":"Electro Optics Control"},{"key":"10.1016\/j.compag.2024.109213_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105348","article-title":"Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation","volume":"173","author":"Vasconez","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2024.109213_b0205","series-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7464","article-title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2023"},{"key":"10.1016\/j.compag.2024.109213_b0210","article-title":"Precision detection of dense plums in orchards using the improved YOLOv4 model","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2024.109213_b0215","article-title":"Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2024.109213_b0220","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.scienta.2013.07.017","article-title":"Phenological growth stages of lychee (Litchi chinensis Sonn.) using the extended BBCH-scale","volume":"161","author":"Wei","year":"2013","journal-title":"Sci. Hortic."},{"key":"10.1016\/j.compag.2024.109213_b0225","article-title":"General e (2)-equivariant steerable cnns","volume":"32","author":"Weiler","year":"2019","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.compag.2024.109213_b0230","unstructured":"M. Weiler, P. Forr\u00e9, E. Verlinde, M. Welling, Coordinate Independent Convolutional Networks-Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. arXiv preprint arXiv:2106.06020 2021 10.48550\/arXiv.2106.06020."},{"key":"10.1016\/j.compag.2024.109213_b0240","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2021.705021","article-title":"Multi-target recognition of bananas and automatic positioning for the inflorescence axis cutting point","volume":"12","author":"Wu","year":"2021","journal-title":"Front. Plant Sci."},{"issue":"1","key":"10.1016\/j.compag.2024.109213_b0250","first-page":"28","article-title":"Evaluation of Female Flower Fertilization and Fruit Setting of 43 Litchi","volume":"46","author":"Yan","year":"2019","journal-title":"Germplasm Resources Guangdong Agricultural Science"},{"issue":"1","key":"10.1016\/j.compag.2024.109213_b0255","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1093\/treephys\/tpac097","article-title":"Function of a non-enzymatic hexokinase LcHXK1 as glucose sensor in regulating litchi fruit abscission","volume":"43","author":"Yi","year":"2023","journal-title":"Tree Physiol."},{"issue":"2","key":"10.1016\/j.compag.2024.109213_b0260","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3390\/plants9020151","article-title":"Molecular events involved in fruitlet abscission in litchi","volume":"9","author":"Zhao","year":"2020","journal-title":"Plants"},{"key":"10.1016\/j.compag.2024.109213_b0265","doi-asserted-by":"crossref","unstructured":"Zimmermann, A. (2014). Representation theory : a homological algebra point of view. algebra & applications. Doi: 10.1007\/978-3-319-07968-4.","DOI":"10.1007\/978-3-319-07968-4"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169924006045?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169924006045?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T13:03:02Z","timestamp":1723294982000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169924006045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":48,"alternative-id":["S0168169924006045"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2024.109213","relation":{},"ISSN":["0168-1699"],"issn-type":[{"type":"print","value":"0168-1699"}],"subject":[],"published":{"date-parts":[[2024,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An automatic tracking method for fruit abscission of litchi using convolutional networks","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2024.109213","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109213"}}