{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T05:24:29Z","timestamp":1749792269288,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811610851"},{"type":"electronic","value":"9789811610868"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-1086-8_41","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T22:58:10Z","timestamp":1616713090000},"page":"463-474","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Based Detection of Rhinoceros Beetle Infestation in Coconut Trees Using Drone Imagery"],"prefix":"10.1007","author":[{"given":"Atharva","family":"Kadethankar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neelam","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Burman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinayaka","family":"Hegde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"41_CR1","unstructured":"Cuckoo for Coconuts: Demand Is Soaring, but Production isn\u2019t Keeping Up (2002). https:\/\/gro-intelligence.com\/insights\/articles\/coconuts-growing-demand-stagnant-production"},{"issue":"3","key":"41_CR2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S1995-7645(11)60078-3","volume":"4","author":"M DebMandal","year":"2011","unstructured":"DebMandal, M., Mandal, S.: Coconut (Cocos nucifera L.: Arecaceae): in health promotion and disease prevention. Asian Pac. J. Trop. Med. 4(3), 241\u20137 (2011)","journal-title":"Asian Pac. J. Trop. Med."},{"issue":"22","key":"41_CR3","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.9734\/ARRB\/2014\/11023","volume":"4","author":"G Manjeri","year":"2014","unstructured":"Manjeri, G., Muhamad, R., Tan, S.G.: Oryctes rhinoceros beetles, an oil palm pest in Malaysia. Annu. Res. Rev. Biol. 4(22), 3429 (2014). SCIENCEDOMAIN International","journal-title":"Annu. Res. Rev. Biol."},{"key":"41_CR4","unstructured":"CABI: Invasive species compendium: Oryctes rhinoceros. CAB International, Wallingford (2018). www.cabi.org\/isc"},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"41_CR6","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"41_CR7","unstructured":"Malek, S., Bazi, Y., Alajlan, N., AlHichri, H., Melgani, F.: Efficient framework for palm tree detection in UAV images. IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens. 7(12), 4692\u20134703 (2014). IEEE"},{"key":"41_CR8","unstructured":"Zakharova, M.: Automated coconut tree detection in aerial imagery using deep learning. Master\u2019s thesis, The Katholieke Universiteit Leuven, L\u00f6wen, Belgium (2017)"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Puttemans, S., Van Beeck, K., Goedem\u00e9, T.: Comparing boosted cascades to deep learning architectures for fast and robust coconut tree detection in aerial images. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 5, pp. 230\u2013241 (2018)","DOI":"10.5220\/0006571902300241"},{"key":"41_CR10","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)"},{"key":"41_CR11","unstructured":"Tzutalin: LabelImg, Free Software: MIT License (2015). https:\/\/www.bibsonomy.org\/bibtex\/24d72bded15249d2d0e3d9dc187d50e16\/slicside"},{"key":"41_CR12","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"de Souza, I.E., Falc\u00e3o, A.X.: Learning CNN filters from user-drawn image markers for coconut-tree image classification. arXiv preprint arXiv:2008.03549 (2020)","DOI":"10.1109\/LGRS.2020.3020098"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Kestur, R., et al.: Tree crown detection, delineation and counting in uav remote sensed images: a neural network based spectral-spatial method. J. Indian Soc. Remote Sens. 46.6, 991\u20131004 (2018)","DOI":"10.1007\/s12524-018-0756-4"},{"key":"41_CR15","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.isprsjprs.2012.01.009","volume":"68","author":"C Hung","year":"2012","unstructured":"Hung, C., Bryson, M., Sukkarieh, S.: Multi-class predictive template for tree crown detection. ISPRS J. Photogramm. Remote Sens. 68, 170\u2013183 (2012)","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"41_CR16","unstructured":"Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1086-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T20:27:03Z","timestamp":1619296023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1086-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811610851","9789811610868"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1086-8_41","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prayagraj","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2020.iiita.ac.in","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"352","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":"134","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":"0","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":"38% - 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":"3","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","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)"}},{"value":"Due to the COVID-19 pandemic the conference was partially held in a virtual mode.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}