{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:03:51Z","timestamp":1743044631956,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490101"},{"type":"electronic","value":"9783031490118"}],"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\/978-3-031-49011-8_31","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T16:03:27Z","timestamp":1702569807000},"page":"388-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmentation as a Pre-processing for Automatic Grape Moths Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-5024","authenticated-orcid":false,"given":"Ana Cl\u00e1udia","family":"Teixeira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7097-3260","authenticated-orcid":false,"given":"Gabriel A.","family":"Carneiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-9153","authenticated-orcid":false,"given":"Raul","family":"Morais","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-930X","authenticated-orcid":false,"given":"Joaquim J.","family":"Sousa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Altimira, F., Vitta, N., Tapia, E.: Integrated pest management of Lobesia botrana with Microorganism in Vineyards: an alternative for clean grapes production (2021). https:\/\/doi.org\/10.5772\/intechopen.99153","DOI":"10.5772\/intechopen.99153"},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"180750","DOI":"10.1109\/ACCESS.2020.3024891","volume":"8","author":"CJ Chen","year":"2020","unstructured":"Chen, C.J., Huang, Y.Y., Li, Y.S., Chang, C.Y., Huang, Y.M.: An AIoT based smart agricultural system for pests detection. IEEE Access 8, 180750\u2013180761 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3024891","journal-title":"IEEE Access"},{"key":"31_CR3","doi-asserted-by":"publisher","unstructured":"Duso, C., Pozzebon, A., Lorenzon, M., Fornasiero, D., Tirello, P., Simoni, S., Bagnoli, B.: The impact of microbial and botanical insecticides on grape berry moths and their effects on secondary pests and beneficials. Agronomy 12(1) (2022). https:\/\/doi.org\/10.3390\/agronomy12010217, https:\/\/www.mdpi.com\/2073-4395\/12\/1\/217","DOI":"10.3390\/agronomy12010217"},{"key":"31_CR4","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"31_CR5","doi-asserted-by":"publisher","unstructured":"Li, W., Wang, D., Li, M., Gao, Y., Wu, J., Yang, X.: Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Comput. Electron. Agric. 183, 106048 (2021). https:\/\/doi.org\/10.1016\/j.compag.2021.106048","DOI":"10.1016\/j.compag.2021.106048"},{"key":"31_CR6","doi-asserted-by":"publisher","unstructured":"Park, Y.H., Choi, S., Kwon, Y.J., Kwon, S.W., Kang, Y., Jun, T.H.: Detection of soybean insect pest and a forecasting platform using deep learning with unmanned ground vehicles. Agronomy 13, 477 (2023). https:\/\/doi.org\/10.3390\/agronomy13020477","DOI":"10.3390\/agronomy13020477"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"31_CR9","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.procs.2023.01.275","volume":"219","author":"AC Teixeira","year":"2023","unstructured":"Teixeira, A.C., Morais, R., Sousa, J.J., Peres, E., Cunha, A.: A deep learning approach for automatic counting of bedbugs and grape moth. Procedia Comput. Sci. 219, 145\u2013152 (2023). https:\/\/doi.org\/10.1016\/j.procs.2023.01.275","journal-title":"Procedia Comput. Sci."},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Teixeira, A.C., Ribeiro, J., Morais, R., Sousa, J.J., Cunha, A.: A systematic review on automatic insect detection using deep learning. Agriculture 13(3) (2023). https:\/\/doi.org\/10.3390\/agriculture13030713, https:\/\/www.mdpi.com\/2077-0472\/13\/3\/713","DOI":"10.3390\/agriculture13030713"},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"\u00dcnl\u00fc, L., Akdemir, B., \u00d6g\u00fcr, E., \u015eahin, : Remote monitoring of European grapevine moth, Lobesia Botrana (Lepidoptera: Tortricidae) population using camera-based pheromone traps in vineyards. Turkish J. Agric. Food Sci. Technol. 7(4), 652\u2013657 (2019). https:\/\/doi.org\/10.24925\/turjaf.v7i4.652-657.2382, http:\/\/www.agrifoodscience.com\/index.php\/TURJAF\/article\/view\/2382","DOI":"10.24925\/turjaf.v7i4.652-657.2382"},{"key":"31_CR12","doi-asserted-by":"publisher","unstructured":"Yun, W., Kumar, J.P., Lee, S., Kim, D.S., Cho, B.K.: Deep learning-based system development for black pine BAST scale detection. Sci. Rep. 12 (2022). https:\/\/doi.org\/10.1038\/s41598-021-04432-z","DOI":"10.1038\/s41598-021-04432-z"},{"key":"31_CR13","doi-asserted-by":"publisher","unstructured":"Zhu, L., Geng, X., Li, Z., Liu, C.: Improving yolov5 with attention mechanism for detecting boulders from planetary images. Remote Sens. 13(18) (2021). https:\/\/doi.org\/10.3390\/rs13183776, https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3776","DOI":"10.3390\/rs13183776"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49011-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T16:10:24Z","timestamp":1702570224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49011-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031490101","9783031490118"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49011-8_31","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":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faial Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2023.inesctec.pt\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","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":"85","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":"52% - 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":"4","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)"}}]}}