{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:26:14Z","timestamp":1775039174064,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031231155","type":"print"},{"value":"9783031231162","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-23116-2_19","type":"book-chapter","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:16:02Z","timestamp":1675163762000},"page":"223-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pest Birds Detection Approach in\u00a0Rice Crops Using Pre-trained YOLOv4 Model"],"prefix":"10.1007","author":[{"given":"Ismael","family":"Diakhaby","sequence":"first","affiliation":[]},{"given":"Mouhamadou Lamine","family":"Ba","sequence":"additional","affiliation":[]},{"given":"Amadou Dahirou","family":"Gueye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"issue":"30","key":"19_CR1","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ifacol.2019.12.483","volume":"52","author":"S Bhusal","year":"2019","unstructured":"Bhusal, S., Bhattarai, U., Karkee, M.: Improving pest bird detection in a vineyard environment using super-resolution and deep learning. IFAC-PapersOnLine 52(30), 18\u201323 (2019)","journal-title":"IFAC-PapersOnLine"},{"key":"19_CR2","unstructured":"Bochkovskiy, A., Wang, C.-Y., Mark Liao, H.-Y.: YOLOv4: optimal speed and accuracy of object detection. CoRR, abs\/2004.10934, 2020"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei, L.F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"9","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., Qu, R.: A survey of deep learning-based object detection. IEEE Access 7, 128837\u2013128868 (2019)","DOI":"10.1109\/ACCESS.2019.2939201"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"19_CR7","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":"19_CR8","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of IEEE and CVF Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"19_CR9","unstructured":"Liu, W., et al.: In: Proceedings of ECCV 9905, 21\u201337 (2016)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Marcon. P., et al.: A system using artificial intelligence to detect and scare bird flocks in the protection of ripening fruit. Sensors 21(12), 4244 (2021)","DOI":"10.3390\/s21124244"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., SilvaEduardo da, A.B.: A survey on performance metrics for object-detection algorithms. In: Proceedings of International Conference on Systems, Signals and Image Processing, pp. 237\u2013242 (2020)","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"19_CR12","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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"19_CR13","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"19_CR15","unstructured":"Suleiman, I., Babawuya, A., Oyewole, A., Salihu, B., Adeoti O., Alhaji, S.Y.: A review of bird pest repellent systems in farms (2021)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedinds of Conference on Computer Vision and Pattern Recognition, Workshops, pp. 1571\u20131580. Computer Vision Foundation and IEEE (2020)","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Abbas Zaidi, S.S., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.: A survey of modern deep learning based object detection models. Digit. Sig. Process. 126, 103514 (2022)","DOI":"10.1016\/j.dsp.2022.103514"},{"issue":"11","key":"19_CR18","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"Z-Q Zhao","year":"2019","unstructured":"Zhao, Z.-Q., Zheng, P., Shou-Tao, X., Xindong, W.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212\u20133232 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR19","unstructured":"Zou, Z., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. CoRR, http:\/\/arxiv.org\/abs\/1905.05055arXiv:1905.05055 (2019)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Innovations and Interdisciplinary Solutions for Underserved Areas"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23116-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:20:53Z","timestamp":1675164053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23116-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031231155","9783031231162"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23116-2_19","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"InterSol","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Abuja","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nigeria","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 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2022","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":"intersol2022","order":10,"name":"conference_id","label":"Conference ID","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":"EAI Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","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":"26","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":"39% - 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":"2","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)"}}]}}