{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:56:45Z","timestamp":1743001005472,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031510229"},{"type":"electronic","value":"9783031510236"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-51023-6_36","type":"book-chapter","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:02:36Z","timestamp":1705993356000},"page":"430-441","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ArcheoWeedNet: Weed Classification in\u00a0the\u00a0Parco archeologico del Colosseo"],"prefix":"10.1007","author":[{"given":"Gaetano","family":"Saurio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Muscas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indro","family":"Spinelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerio","family":"Rughetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irma","family":"Della Giovampaola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Scardapane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2193-1801-2-660","volume":"2","author":"JG Arnal Barbedo","year":"2013","unstructured":"Arnal Barbedo, J.G.: Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1), 1\u201312 (2013)","journal-title":"Springerplus"},{"issue":"11","key":"36_CR2","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.3390\/s21113758","volume":"21","author":"L Benos","year":"2021","unstructured":"Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., Bochtis, D.: Machine learning in agriculture: a comprehensive updated review. Sensors 21(11), 3758 (2021)","journal-title":"Sensors"},{"issue":"3","key":"36_CR3","doi-asserted-by":"publisher","first-page":"748","DOI":"10.3390\/agronomy12030748","volume":"12","author":"A Cravero","year":"2022","unstructured":"Cravero, A., Pardo, S., Sep\u00falveda, S., Mu\u00f1oz, L.: Challenges to use machine learning in agricultural big data: a systematic literature review. Agronomy 12(3), 748 (2022)","journal-title":"Agronomy"},{"key":"36_CR4","unstructured":"Della Giovampaola, I.: Piano sostenibile di tutela e valorizzazione del patrimonio archeologico e di educazione continua al patrimonio culturale: SyPEAH (A platform Systemfor the Protection and Education of Archaeological Heritage). Bullettino della Commissione Archeologica Comunale CXII, pp. 61\u201376 (2021)"},{"issue":"6","key":"36_CR5","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/geosciences11060246","volume":"11","author":"I Della Giovampaola","year":"2021","unstructured":"Della Giovampaola, I.: SyPEAH: the WebAPP system for protection and education to archaeological heritage in the parco archeologico del colosseo. Geosciences 11(6), 246 (2021)","journal-title":"Geosciences"},{"key":"36_CR6","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":"36_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"issue":"5","key":"36_CR8","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.3390\/agronomy12051079","volume":"12","author":"M Fracchiolla","year":"2022","unstructured":"Fracchiolla, M., Lasorella, C., Cazzato, E., Vurro, M.: Weeds in non-agricultural areas: how to evaluate the impact? A preliminary case study in archaeological sites. Agronomy 12(5), 1079 (2022)","journal-title":"Agronomy"},{"key":"36_CR9","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":"36_CR10","doi-asserted-by":"crossref","unstructured":"Angiosperm Phylogeny Group: An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG iv. Bot. J. Linn. Soc. 181(1), 1\u201320 (2016)","DOI":"10.1111\/boj.12385"},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"106067","DOI":"10.1016\/j.compag.2021.106067","volume":"184","author":"AM Hasan","year":"2021","unstructured":"Hasan, A.M., Sohel, F., Diepeveen, D., Laga, H., Jones, M.G.: A survey of deep learning techniques for weed detection from images. Comput. Electron. Agric. 184, 106067 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7553","key":"36_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 722\u2013729. IEEE (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"issue":"1","key":"36_CR15","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1038\/s41598-018-38343-3","volume":"9","author":"A Olsen","year":"2019","unstructured":"Olsen, A., et al.: DeepWeeds: a multiclass weed species image dataset for deep learning. Sci. Rep. 9(1), 2058 (2019)","journal-title":"Sci. Rep."},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Panda, B., Mishra, M.K., Mishra, B.S.P., Tiwari, A.K.: An extensive review on crop\/weed classification models. In: Web Intelligence, pp. 1\u201316. No. Preprint, IOS Press (2023)","DOI":"10.3233\/WEB-220115"},{"issue":"2","key":"36_CR17","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0169-2046(01)00187-6","volume":"57","author":"C Ricotta","year":"2001","unstructured":"Ricotta, C., Grapow, L.C., Avena, G., Blasi, C.: Topological analysis of the spatial distribution of plant species richness across the city of Rome (Italy) with the echelon approach. Landsc. Urban Plan. 57(2), 69\u201376 (2001)","journal-title":"Landsc. Urban Plan."},{"key":"36_CR18","unstructured":"Russo, A., Della Giovampaola, I.: Il monitoraggio e la manutenzione delle aree archeologiche. Il piano per il futuro del Parco archeologico del Colosseo, pp. 13\u201331. \u201cL\u2019Erma\u201d di Bretschneider (2020)"},{"key":"36_CR19","doi-asserted-by":"publisher","unstructured":"Russo, A., Giovampaola, I.D., Spizzichino, D., Leoni, G., Coletta, A., Virelli, M.: The project of parco archeologico del colosseo and the Italian network of archaeological parks: from satellite monitoring to conservation and preventive maintenance policies. In: El-Qady, G.M., Margottini, C. (eds.) Sustainable Conservation of UNESCO and Other Heritage Sites Through Proactive Geosciences, pp. 659\u2013678. Springer, Cham(2023). https:\/\/doi.org\/10.1007\/978-3-031-13810-2_34","DOI":"10.1007\/978-3-031-13810-2_34"},{"key":"36_CR20","doi-asserted-by":"publisher","first-page":"105833","DOI":"10.1016\/j.dib.2020.105833","volume":"31","author":"K Sudars","year":"2020","unstructured":"Sudars, K., Jasko, J., Namatevs, I., Ozola, L., Badaukis, N.: Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief 31, 105833 (2020)","journal-title":"Data Brief"},{"key":"36_CR21","doi-asserted-by":"publisher","first-page":"7361042","DOI":"10.1155\/2017\/7361042","volume":"2017","author":"Y Sun","year":"2017","unstructured":"Sun, Y., Liu, Y., Wang, G., Zhang, H., et al.: Deep learning for plant identification in natural environment. Comput. Intell. Neurosci. 2017, 7361042 (2017)","journal-title":"Comput. Intell. Neurosci."},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Therrien, R., Doyle, S.: Role of training data variability on classifier performance and generalizability. In: Medical Imaging 2018: Digital Pathology, vol. 10581, pp. 58\u201370. SPIE (2018)","DOI":"10.1117\/12.2293919"},{"key":"36_CR23","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11831-016-9206-z","volume":"25","author":"J W\u00e4ldchen","year":"2018","unstructured":"W\u00e4ldchen, J., M\u00e4der, P.: Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25, 507\u2013543 (2018)","journal-title":"Arch. Comput. Methods Eng."},{"issue":"11","key":"36_CR24","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.3390\/s21113647","volume":"21","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Chen, Y., Zhao, B., Kang, X., Ding, Y.: Review of weed detection methods based on computer vision. Sensors 21(11), 3647 (2021)","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-51023-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:08:13Z","timestamp":1705993693000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-51023-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031510229","9783031510236"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-51023-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"24 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 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":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.org\/","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":"144","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":"82","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":"13","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":"57% - 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":"3","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":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","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)"}}]}}