{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T14:29:36Z","timestamp":1759847376831,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031360206"},{"type":"electronic","value":"9783031360213"}],"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\/978-3-031-36021-3_18","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T09:06:16Z","timestamp":1688115976000},"page":"196-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating Chlorophyll Content from\u00a0Hyperspectral Data Using Gradient Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-1778","authenticated-orcid":false,"given":"Bogdan","family":"Ruszczak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6180-9979","authenticated-orcid":false,"given":"Agata M.","family":"Wijata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-1569","authenticated-orcid":false,"given":"Jakub","family":"Nalepa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., et al.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9(11) (2017)","DOI":"10.3390\/rs9111110"},{"key":"18_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.07.009","volume":"85","author":"J Chen","year":"2013","unstructured":"Chen, J., Lu, M., Chen, X., Chen, J., Chen, L.: A spectral gradient difference based approach for land cover change detection. ISPRS J. Photogramm. Remote. Sens. 85, 1\u201312 (2013)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"12","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1010718","volume":"18","author":"D Chicco","year":"2022","unstructured":"Chicco, D., Oneto, L., Tavazzi, E.: Eleven quick tips for data cleaning and feature engineering. PLoS Comput. Biol. 18(12), 1\u201321 (2022)","journal-title":"PLoS Comput. Biol."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Gorretta, N., Nouri, M., Herrero, A., Gowen, A., Roger, J.M.: Early detection of the fungal disease \u201capple scab\u201d using SWIR hyperspectral imaging. In: 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1\u20134 (2019)","DOI":"10.1109\/WHISPERS.2019.8921066"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Guo, C., et al.: Predicting Fv\/Fm and evaluating cotton drought tolerance using hyperspectral and 1D-CNN. Front. Plant Sci. 13, 3700 (2022)","DOI":"10.3389\/fpls.2022.1007150"},{"issue":"1","key":"18_CR6","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1080\/22797254.2021.2010605","volume":"55","author":"NH Huynh","year":"2022","unstructured":"Huynh, N.H., B\u00f6er, G., Schramm, H.: Self-attention and generative adversarial networks for algae monitoring. Eur. J. Remote Sens. 55(1), 10\u201322 (2022)","journal-title":"Eur. J. Remote Sens."},{"issue":"1","key":"18_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","volume":"8","author":"X Jin","year":"2020","unstructured":"Jin, X., Li, Z., Feng, H., Ren, Z., Li, S.: Deep neural network algorithm for estimating maize biomass based on simulated sentinel 2A vegetation indices and leaf area index. Crop J. 8(1), 87\u201397 (2020)","journal-title":"Crop J."},{"issue":"8","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/LGRS.2019.2895697","volume":"16","author":"J Nalepa","year":"2019","unstructured":"Nalepa, J., Myller, M., Kawulok, M.: Validating hyperspectral image segmentation. IEEE Geosci. Remote Sens. Lett. 16(8), 1264\u20131268 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-030-52624-5_14","volume-title":"Smart Sensors for Industrial Internet of Things: Challenges, Solutions and Applications","author":"V Ponnusamy","year":"2021","unstructured":"Ponnusamy, V., Natarajan, S.: Precision agriculture using advanced technology of IoT, unmanned aerial vehicle, augmented reality, and machine learning. In: Gupta, D., de Hugo, C., Albuquerque, V., Khanna, A., Mehta, P.L. (eds.) Smart Sensors for Industrial Internet of Things: Challenges, Solutions and Applications, pp. 207\u2013229. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-52624-5_14"},{"key":"18_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2022.108087","volume":"42","author":"B Ruszczak","year":"2022","unstructured":"Ruszczak, B., Boguszewska-Ma\u0144kowska, D.: Deep potato - the hyperspectral imagery of potato cultivation with reference agronomic measurements dataset: towards potato physiological features modeling. Data Brief 42, 108087 (2022)","journal-title":"Data Brief"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Ruszczak, B., Wijata, A.M., Nalepa, J.: Unbiasing the estimation of chlorophyll from hyperspectral images: a benchmark dataset, validation procedure and baseline results. Remote Sens. 14(21) (2022)","DOI":"10.3390\/rs14215526"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12524-019-00969-9","volume":"47","author":"G Singhal","year":"2019","unstructured":"Singhal, G., Bansod, B., Mathew, L., Goswami, J., Choudhury, B., Raju, P.: Estimation of leaf chlorophyll concentration in turmeric (curcuma longa) using high-resolution unmanned aerial vehicle imagery based on kernel ridge regression. J. Indian Soc. Remote Sens. 47, 1\u201312 (2019)","journal-title":"J. Indian Soc. Remote Sens."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., Singh, S.K.: Applications of remote sensing in precision agriculture: a review. Remote Sens. 12(19) (2020)","DOI":"10.3390\/rs12193136"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","volume":"154","author":"J Wang","year":"2019","unstructured":"Wang, J., et al.: Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS J. Photogramm. Remote. Sens. 154, 189\u2013201 (2019)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Wen, S., Shi, N., Lu, J., Gao, Q., Yang, H., Gao, Z.: Estimating chlorophyll fluorescence parameters of rice (Oryza sativa L.) based on spectrum transformation and a joint feature extraction algorithm. Agronomy 13(2) (2023)","DOI":"10.3390\/agronomy13020337"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Yan, T., et al.: Combining multi-dimensional convolutional neural network (CNN) with visualization method for detection of aphis gossypii glover infection in cotton leaves using hyperspectral imaging. Front. Plant Sci. 12 (2021)","DOI":"10.3389\/fpls.2021.604510"},{"issue":"5","key":"18_CR17","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1080\/01431161.2020.1826057","volume":"42","author":"J Yue","year":"2021","unstructured":"Yue, J., Zhou, C., Guo, W., Feng, H., Xu, K.: Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images. Int. J. Remote Sens. 42(5), 1602\u20131622 (2021)","journal-title":"Int. J. Remote Sens."},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Detection of canopy chlorophyll content of corn based on continuous wavelet transform analysis. Remote Sens. 12(17) (2020)","DOI":"10.3390\/rs12172741"},{"key":"18_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2021.107985","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images. Ecol. Ind. 129, 107985 (2021)","journal-title":"Ecol. Ind."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36021-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T09:08:17Z","timestamp":1688116097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36021-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360206","9783031360213"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36021-3_18","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":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","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":"188","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":"94","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":"35% - 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":"2,8","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":"3,2","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)"}}]}}