{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T08:08:24Z","timestamp":1743754104328,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030598297"},{"type":"electronic","value":"9783030598303"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59830-3_19","type":"book-chapter","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T23:04:34Z","timestamp":1602198274000},"page":"218-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1728-4076","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-8233","authenticated-orcid":false,"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1091-9091","authenticated-orcid":false,"given":"Cheng-yuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8561-9299","authenticated-orcid":false,"given":"Xiao","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,9]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patcog.2017.03.020","volume":"75","author":"X Bai","year":"2018","unstructured":"Bai, X., Yan, C., Yang, H., Bai, L., Zhou, J., Hancock, E.R.: Adaptive hash retrieval with kernel based similarity. Pattern Recogn. 75, 136\u2013148 (2018)","journal-title":"Pattern Recogn."},{"issue":"1","key":"19_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11119-012-9292-3","volume":"14","author":"R Bansal","year":"2013","unstructured":"Bansal, R., Lee, W.S., Satish, S.: Green citrus detection using fast Fourier transform (FFT) leakage. Precis. Agric. 14(1), 59\u201370 (2013)","journal-title":"Precis. Agric."},{"issue":"6","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1002\/rob.21699","volume":"34","author":"S Bargoti","year":"2017","unstructured":"Bargoti, S., Underwood, J.P.: Image segmentation for fruit detection and yield estimation in apple orchards. J. Field Rob. 34(6), 1039\u20131060 (2017)","journal-title":"J. Field Rob."},{"issue":"3","key":"19_CR4","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1111\/j.1755-0238.2004.tb00022.x","volume":"10","author":"GM Dunn","year":"2004","unstructured":"Dunn, G.M., Martin, S.R.: Yield prediction from digital image analysis: a technique with potential for vineyard assessments prior to harvest. Aust. J. Grape Wine Res. 10(3), 196\u2013198 (2004)","journal-title":"Aust. J. Grape Wine Res."},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","volume":"116","author":"A Gongal","year":"2015","unstructured":"Gongal, A., Amatya, S., Karkee, M., Zhang, Q., Lewis, K.: Sensors and systems for fruit detection and localization: a review. Comput. Electron. Agric. 116, 8\u201319 (2015)","journal-title":"Comput. Electron. Agric."},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"issue":"10","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1016\/S0031-3203(98)00170-8","volume":"32","author":"AR Jim\u00e9nez","year":"1999","unstructured":"Jim\u00e9nez, A.R., Jain, A.K., Ceres, R., Pons, J.L.: Automatic fruit recognition: a survey and new results using range\/attenuation images. Pattern Recogn. 32(10), 1719\u20131736 (1999)","journal-title":"Pattern Recogn."},{"key":"19_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980"},{"key":"19_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"19_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.biosystemseng.2018.04.009","volume":"171","author":"J Lu","year":"2018","unstructured":"Lu, J., Lee, W.S., Gan, H., Hu, X.: Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis. Biosyst. Eng. 171, 78\u201390 (2018)","journal-title":"Biosyst. Eng."},{"issue":"5","key":"19_CR12","doi-asserted-by":"publisher","first-page":"3976","DOI":"10.1109\/TIE.2017.2764849","volume":"65","author":"C Luo","year":"2017","unstructured":"Luo, C., Yu, L., Ren, P.: A vision-aided approach to perching a bioinspired unmanned aerial vehicle. IEEE Trans. Ind. Electron. 65(5), 3976\u20133984 (2017)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"19_CR13","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024\u20138035 (2019)"},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.compag.2012.11.009","volume":"91","author":"AB Payne","year":"2013","unstructured":"Payne, A.B., Walsh, K.B., Subedi, P., Jarvis, D.: Estimation of mango crop yield using image analysis-segmentation method. Comput. Electron. Agric. 91, 57\u201364 (2013)","journal-title":"Comput. Electron. Agric."},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Rabatel, G., Guizard, C.: Grape berry calibration by computer vision using elliptical model fitting. In: European Conference on Precision Agriculture, vol. 6, pp. 581\u2013587 (2007)","DOI":"10.3920\/9789086866038_070"},{"issue":"4","key":"19_CR16","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3390\/s17040905","volume":"17","author":"M Rahnemoonfar","year":"2017","unstructured":"Rahnemoonfar, M., Sheppard, C.: Deep count: fruit counting based on deep simulated learning. Sensors 17(4), 905 (2017)","journal-title":"Sensors"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"19_CR18","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"issue":"8","key":"19_CR19","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.3390\/s16081222","volume":"16","author":"I Sa","year":"2016","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., McCool, C.: Deepfruits: a fruit detection system using deep neural networks. Sensors 16(8), 1222 (2016)","journal-title":"Sensors"},{"key":"19_CR20","unstructured":"Sengupta, S., Lee, W.S.: Identification and determination of the number of green citrus fruit under different ambient light conditions. In: International Conference of Agricultural Engineering (2012)"},{"key":"19_CR21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.biosystemseng.2013.12.008","volume":"118","author":"Y Song","year":"2014","unstructured":"Song, Y., Glasbey, C., Horgan, G., Polder, G., Dieleman, J., Van der Heijden, G.: Automatic fruit recognition and counting from multiple images. Biosyst. Eng. 118, 203\u2013215 (2014)","journal-title":"Biosyst. Eng."},{"issue":"2","key":"19_CR22","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/LGRS.2018.2872355","volume":"16","author":"C Wang","year":"2018","unstructured":"Wang, C., Bai, X., Wang, S., Zhou, J., Ren, P.: Multiscale visual attention networks for object detection in VHR remote sensing images. IEEE Geosci. Rem. Sens. Lett. 16(2), 310\u2013314 (2018)","journal-title":"IEEE Geosci. Rem. Sens. Lett."},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.patrec.2019.09.021","volume":"128","author":"C Wang","year":"2019","unstructured":"Wang, C., Wang, X., Bai, X., Liu, Y., Zhou, J.: Self-supervised deep homography estimation with invertibility constraints. Pattern Recogn. Lett. 128, 355\u2013360 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., Hancock, E.: Deep hashing by discriminating hard examples. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1535\u20131542 (2019)","DOI":"10.1145\/3343031.3350927"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, L., Xiao, B., Liu, X., Zhou, J., Hancock, E.R., et al.: Latent distribution preserving deep subspace clustering. In: 28th International Joint Conference on Artificial Intelligence, New York (2019)","DOI":"10.24963\/ijcai.2019\/617"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59830-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:30:43Z","timestamp":1710250243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59830-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598297","9783030598303"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59830-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"9 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPRAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhongshan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icprai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icprai2020.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1,99","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}