{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:29:50Z","timestamp":1742984990609,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250743"},{"type":"electronic","value":"9783031250750"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25075-0_51","type":"book-chapter","created":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T09:16:53Z","timestamp":1676798213000},"page":"751-764","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards the\u00a0Computational Assessment of\u00a0the\u00a0Conservation Status of\u00a0a\u00a0Habitat"],"prefix":"10.1007","author":[{"given":"X. Huy","family":"Manh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Gigante","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Angiolini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simonetta","family":"Bagella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Caccianiga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Franco","family":"Angelini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manolo","family":"Garabini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Remagnino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,19]]},"reference":[{"key":"51_CR1","doi-asserted-by":"crossref","unstructured":"Bonari, G.EA.: Shedding light on typical species: implications for habitat monitoringl. Plant Sociol. 58(1), 157\u2013166 (2021)","DOI":"10.3897\/pls2020581\/08"},{"key":"51_CR2","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. corr abs\/1610.02357 (2016). arXiv preprint arXiv:1610.02357 (2016)","DOI":"10.1109\/CVPR.2017.195"},{"key":"51_CR4","doi-asserted-by":"crossref","unstructured":"Enkvetchakul, P., Surinta, O.: Effective data augmentation and training techniques for improving deep learning in plant leaf disease recognition. Appl. Sci. Eng. Progr. (2021)","DOI":"10.14416\/j.asep.2021.01.003"},{"key":"51_CR5","doi-asserted-by":"crossref","unstructured":"Gao, Z., Li, M., Li, W., Yan, Q.: Classification of flowers under complex background using inception-v3 network. In: Proceedings of the 2020 4th International Conference on Deep Learning Technologies (ICDLT), pp. 113\u2013117 (2020)","DOI":"10.1145\/3417188.3417192"},{"key":"51_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.01.091","volume":"292","author":"F Garcia-Lamont","year":"2018","unstructured":"Garcia-Lamont, F., Cervantes, J., L\u00f3pez, A., Rodriguez, L.: Segmentation of images by color features: a survey. Neurocomputing 292, 1\u201327 (2018)","journal-title":"Neurocomputing"},{"key":"51_CR7","unstructured":"Garcin, C., et al.: Pl@ ntnet-300k: a plant image dataset with high label ambiguity and a long-tailed distribution. In: NeurIPS 2021\u201335th Conference on Neural Information Processing Systems (2021)"},{"key":"51_CR8","first-page":"77","volume":"53","author":"D Gigante","year":"2016","unstructured":"Gigante, D., et al.: A methodological protocol for annex in habitat monitoring: the contribution of vegetation science. Plant Sociol. 53, 77\u201387 (2016)","journal-title":"Plant Sociol."},{"key":"51_CR9","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","volume":"125","author":"E Hamuda","year":"2016","unstructured":"Hamuda, E., Glavin, M., Jones, E.: A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184\u2013199 (2016)","journal-title":"Comput. Electron. Agric."},{"key":"51_CR10","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arxiv 2015. arXiv preprint arXiv:1512.03385 (2015)"},{"key":"51_CR11","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","volume":"30","author":"PT Jiang","year":"2021","unstructured":"Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875\u20135888 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"51_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-020-00698-y","volume":"16","author":"Y Jiang","year":"2020","unstructured":"Jiang, Y., Li, C., Xu, R., Sun, S., Robertson, J.S., Paterson, A.H.: Deepflower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field. Plant Methods 16(1), 1\u201317 (2020)","journal-title":"Plant Methods"},{"key":"51_CR13","doi-asserted-by":"crossref","unstructured":"Jongman, R.: Biodiversity observation from local to global. Ecol. Indicators 33, 1\u20134 (2013)","DOI":"10.1016\/j.ecolind.2013.03.012"},{"key":"51_CR14","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compag.2019.01.041","volume":"158","author":"A Kaya","year":"2019","unstructured":"Kaya, A., Keceli, A.S., Catal, C., Yalic, H.Y., Temucin, H., Tekinerdogan, B.: Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 158, 20\u201329 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"51_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"51_CR16","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Chan, C.S., Wilkin, P., Remagnino, P.: Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 452\u2013456. IEEE (2015)","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"51_CR17","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":"T-Y 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":"51_CR18","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patrec.2015.10.013","volume":"81","author":"M Minervini","year":"2016","unstructured":"Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn. Lett. 81, 80\u201389 (2016)","journal-title":"Pattern Recogn. Lett."},{"key":"51_CR19","unstructured":"Nguyen, T.T.N., Le, V., Le, T., Hai, V., Pantuwong, N., Yagi, Y.: Flower species identification using deep convolutional neural networks. In: AUN\/SEED-Net Regional Conference for Computer and Information Engineering (2016)"},{"issue":"1","key":"51_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"51_CR21","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"51_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53745-9","volume-title":"Computational Botany","author":"P Remagnino","year":"2016","unstructured":"Remagnino, P., Mayo, S., Wilkin, P., Cope, J., Kirkup, D.: Computational Botany. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-53745-9"},{"key":"51_CR23","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, vol. 28 (2015)"},{"key":"51_CR24","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"51_CR25","doi-asserted-by":"crossref","unstructured":"Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-Cam: visual explanations from deep networks via gradient-based localization. arXiv preprint arXiv:1610.02391 (2016)","DOI":"10.1109\/ICCV.2017.74"},{"key":"51_CR26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842, p. 1409 (2014)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"51_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"51_CR28","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"issue":"1","key":"51_CR29","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25075-0_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T09:38:00Z","timestamp":1676799480000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25075-0_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250743","9783031250750"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25075-0_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}