{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:36:37Z","timestamp":1773437797820,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031591662","type":"print"},{"value":"9783031591679","type":"electronic"}],"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\/978-3-031-59167-9_26","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T07:02:44Z","timestamp":1714114964000},"page":"311-323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multispectral Image Segmentation in\u00a0Agriculture: A Comprehensive Study on\u00a0Fusion Approaches"],"prefix":"10.1007","author":[{"given":"Nuno","family":"Cunha","sequence":"first","affiliation":[]},{"given":"Tiago","family":"Barros","sequence":"additional","affiliation":[]},{"given":"M\u00e1rio","family":"Reis","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Marta","sequence":"additional","affiliation":[]},{"given":"Cristiano","family":"Premebida","sequence":"additional","affiliation":[]},{"given":"Urbano J.","family":"Nunes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.patrec.2017.09.038","volume":"115","author":"A Asvadi","year":"2018","unstructured":"Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., Nunes, U.J.: Multimodal vehicle detection: fusing 3D-lidar and color camera data. Pattern Recognit. Lett. 115, 20\u201329 (2018)","journal-title":"Pattern Recognit. Lett."},{"key":"26_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106782","volume":"195","author":"T Barros","year":"2022","unstructured":"Barros, T., et al.: Multispectral vineyard segmentation: a deep learning comparison study. Comput. Electron. Agric. 195, 106782 (2022)","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"26_CR3","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","volume":"22","author":"D Feng","year":"2021","unstructured":"Feng, D., et al.: Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges. IEEE Trans. Intell. Transp. Syst. 22(3), 1341\u20131360 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation (2017)","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Jameel, S.M., Gilal, A.R., Rizvi, S.S.H., Rehman, M., Hashmani, M.A.: Practical implications and challenges of multispectral image analysis. In: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/iCoMET48670.2020.9073821"},{"issue":"3","key":"26_CR6","doi-asserted-by":"publisher","first-page":"143","DOI":"10.14445\/22315381\/IJETT-V7P262","volume":"7","author":"S Karthick","year":"2014","unstructured":"Karthick, S., Sathiyasekar, K., Puraneeswari, A.: A survey based on region based segmentation. Int. J. Eng. Trends Technol. 7(3), 143\u2013147 (2014)","journal-title":"Int. J. Eng. Trends Technol."},{"key":"26_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105446","volume":"174","author":"M Kerkech","year":"2020","unstructured":"Kerkech, M., Hafiane, A., Canals, R.: Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Comput. Electron. Agric. 174, 105446 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"26_CR8","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":"26_CR9","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Milioto, A., Lottes, P., Stachniss, C.: Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2229\u20132235 (2018)","DOI":"10.1109\/ICRA.2018.8460962"},{"issue":"7","key":"26_CR11","first-page":"3523","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"26_CR12","first-page":"259","volume":"3","author":"R Muthukrishnan","year":"2011","unstructured":"Muthukrishnan, R., Radha, M.: Edge detection techniques for image segmentation. Int. J. Comput. Sci. Inform. Technol. 3(6), 259 (2011)","journal-title":"Int. J. Comput. Sci. Inform. Technol."},{"key":"26_CR13","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"3","key":"26_CR14","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MRA.2020.3012492","volume":"28","author":"A Pretto","year":"2021","unstructured":"Pretto, A., et al.: Building an aerial-ground robotics system for precision farming: an adaptable solution. IEEE Robot. Autom. Mag. 28(3), 29\u201349 (2021). https:\/\/doi.org\/10.1109\/MRA.2020.3012492","journal-title":"IEEE Robot. Autom. Mag."},{"issue":"2","key":"26_CR15","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/0734-189X(88)90022-9","volume":"41","author":"P Sahoo","year":"1988","unstructured":"Sahoo, P., Soltani, S., Wong, A.: A survey of thresholding techniques. Comput. Vis. Graphics Image Process. 41(2), 233\u2013260 (1988)","journal-title":"Comput. Vis. Graphics Image Process."},{"key":"26_CR16","unstructured":"Valada, A., Oliveira, G., Brox, T., Burgard, W.: Towards robust semantic segmentation using deep fusion. In: Robotics: Science and systems (RSS 2016) Workshop, are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics, vol. 114 (2016)"},{"key":"26_CR17","series-title":"Springer Proceedings in Advanced Robotics","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/978-3-319-50115-4_41","volume-title":"2016 International Symposium on Experimental Robotics","author":"A Valada","year":"2017","unstructured":"Valada, A., Oliveira, G.L., Brox, T., Burgard, W.: Deep multispectral semantic scene understanding of forested environments using multimodal fusion. In: Kuli\u0107, D., Nakamura, Y., Khatib, O., Venture, G. (eds.) ISER 2016. SPAR, vol. 1, pp. 465\u2013477. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-50115-4_41"},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","volume":"304","author":"H Yu","year":"2018","unstructured":"Yu, H., et al.: Methods and datasets on semantic segmentation: a review. Neurocomputing 304, 82\u2013103 (2018)","journal-title":"Neurocomputing"},{"key":"26_CR19","doi-asserted-by":"publisher","first-page":"7422","DOI":"10.1109\/JSTARS.2021.3098678","volume":"14","author":"K Yuan","year":"2021","unstructured":"Yuan, K., Zhuang, X., Schaefer, G., Feng, J., Guan, L., Fang, H.: Deep-learning-based multispectral satellite image segmentation for water body detection. IEEE J. Sel. Topics Appl. Earth Observations Remote Sens. 14, 7422\u20137434 (2021)","journal-title":"IEEE J. Sel. Topics Appl. Earth Observations Remote Sens."},{"key":"26_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104042","volume":"105","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Sidib\u00e9, D., Morel, O., M\u00e9riaudeau, F.: Deep multimodal fusion for semantic image segmentation: a survey. Image Vis. Comput. 105, 104042 (2021)","journal-title":"Image Vis. Comput."}],"container-title":["Lecture Notes in Networks and Systems","Robot 2023: Sixth Iberian Robotics Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-59167-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T07:12:15Z","timestamp":1714115535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-59167-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031591662","9783031591679"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-59167-9_26","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Robotics conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","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":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robot2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iberianroboticsconf.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}