{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T14:59:03Z","timestamp":1766415543119,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031048807"},{"type":"electronic","value":"9783031048814"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-04881-4_11","type":"book-chapter","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T19:02:54Z","timestamp":1650913374000},"page":"131-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Detection Models for\u00a0Measuring Epidermal Bladder Cells"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4434-4764","authenticated-orcid":false,"given":"Angela","family":"Casado-Garc\u00eda","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6488-9572","authenticated-orcid":false,"given":"Aitor","family":"Agirresarobe","sequence":"additional","affiliation":[]},{"given":"Jon","family":"Miranda-Apodaca","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-1306","authenticated-orcid":false,"given":"J\u00f3nathan","family":"Heras","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9081-7652","authenticated-orcid":false,"given":"Usue","family":"P\u00e9rez-L\u00f3pez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1093\/jxb\/erm057","volume":"58","author":"S Agarie","year":"2007","unstructured":"Agarie, S., et al.: Salt tolerance, salt accumulation, and ionic homeostasis in an epidermal bladder-cell-less mutant of the common ice plant Mesembryanthemum crystallinum. J. Exp. Bot. 58, 1957\u20131967 (2007)","journal-title":"J. Exp. Bot."},{"doi-asserted-by":"publisher","unstructured":"Arvidsson, I., et al.: Generalization of prostate cancer classification for multiple sites using deep learning. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 191\u2013194. IEEE (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363552","key":"11_CR2","DOI":"10.1109\/ISBI.2018.8363552"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"435","DOI":"10.3389\/fpls.2015.00435","volume":"6","author":"BJ Barkla","year":"2015","unstructured":"Barkla, B.J., Vera-Estrella, R.: Single cell-type comparative metabolomics of epidermal bladder cells from the halophyte Mesembryanthemum crystallinum. Front. Plant Sci. 6, 435 (2015)","journal-title":"Front. Plant Sci."},{"unstructured":"Bochkovskiy, A., et al.: YOLOv4: optimal speed and accuracy of object detection. CoRR abs\/2004.10934 (2020)","key":"11_CR4"},{"doi-asserted-by":"publisher","unstructured":"Casado-Garc\u00eda, A., et al.: LabelStoma: a tool for stomata detection based on the YOLO algorithm. Comput. Electron. Agric. 178, 105751 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105751","key":"11_CR5","DOI":"10.1016\/j.compag.2020.105751"},{"doi-asserted-by":"publisher","unstructured":"Chu, P., Li, Z., Lammers, K., Lu, R., Liu, X.: Deep learning-based apple detection using a suppression mask R-CNN. Pattern Recogn. Lett. 147, 206\u2013211 (2021). https:\/\/doi.org\/10.1016\/j.patrec.2021.04.022. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167865521001616","key":"11_CR6","DOI":"10.1016\/j.patrec.2021.04.022"},{"unstructured":"Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Academic Press, Cambridge (1969)","key":"11_CR7"},{"doi-asserted-by":"crossref","unstructured":"Cohen, J.: Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educ. Psychol. Measur. 33, 107\u2013112 (1973)","key":"11_CR8","DOI":"10.1177\/001316447303300111"},{"doi-asserted-by":"publisher","unstructured":"Cynthia, S.T., et al.: Automated detection of plant diseases using image processing and faster R-CNN algorithm. In: Proceedings of 2019 International Conference on Sustainable Technologies for Industry 4.0. STI 2019 (2019). https:\/\/doi.org\/10.1109\/STI47673.2019.9068092","key":"11_CR9","DOI":"10.1109\/STI47673.2019.9068092"},{"doi-asserted-by":"crossref","unstructured":"Dassanayake, M., Larkin, J.C.: Making plants break a sweat: the structure, function, and evolution of plant salt glands. Front. Plant Sci. 8, 406 (2017)","key":"11_CR10","DOI":"10.3389\/fpls.2017.00724"},{"doi-asserted-by":"crossref","unstructured":"Garcia, S., et al.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180, 2044\u20132064 (2010)","key":"11_CR11","DOI":"10.1016\/j.ins.2009.12.010"},{"key":"11_CR12","first-page":"65","volume":"6","author":"OS Holm","year":"1979","unstructured":"Holm, O.S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65\u201370 (1979)","journal-title":"Scand. J. Stat."},{"doi-asserted-by":"crossref","unstructured":"Imamura, T., et al.: A novel WD40-repeat protein involved in formation of epidermal bladder cells in the halophyte quinoa. Commun. Biol. 3, 513 (2020)","key":"11_CR13","DOI":"10.1038\/s42003-020-01249-w"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10725-019-00519-w","volume":"89","author":"SV Isayenkov","year":"2019","unstructured":"Isayenkov, S.V.: Genetic sources for the development of salt tolerance in crops. Plant Growth Regul. 89(1), 1\u201317 (2019). https:\/\/doi.org\/10.1007\/s10725-019-00519-w","journal-title":"Plant Growth Regul."},{"doi-asserted-by":"crossref","unstructured":"Kiani-Pouya, A., et al.: Epidermal bladder cells confer salinity stress tolerance in the halophyte quinoa and Atriplex species. Plant Cell Environ. 40, 1900\u20131915 (2017)","key":"11_CR15","DOI":"10.1111\/pce.12995"},{"doi-asserted-by":"crossref","unstructured":"Kiani-Pouya, A., et al.: A large-scale screening of quinoa accessions reveals an important role of epidermal bladder cells and stomatal patterning in salinity tolerance. Environ. Exp. Bot. 168, 103885 (2019)","key":"11_CR16","DOI":"10.1016\/j.envexpbot.2019.103885"},{"unstructured":"Levene, H.: chap. Robust tests for equality of variances. In: Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, pp. 278\u2013292. Stanford University Press, USA (1960)","key":"11_CR17"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1071\/FP11088","volume":"38","author":"F Orsini","year":"2011","unstructured":"Orsini, F., et al.: Beyond the ionic and osmotic response to salinity in Chenopodium quinoa: functional elements of successful halophytism. Funct. Plant Biol. 38, 818\u2013831 (2011)","journal-title":"Funct. Plant Biol."},{"doi-asserted-by":"publisher","unstructured":"Pratama, M.T., et al.: Deep learning-based object detection for crop monitoring in soybean fields. In: Proceedings of 2020 International Joint Conference on Neural Networks. IJCNN 2020 (2020). https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207400","key":"11_CR19","DOI":"10.1109\/IJCNN48605.2020.9207400"},{"doi-asserted-by":"crossref","unstructured":"Razavian, A.S., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPRW 2014, pp. 512\u2013519 (2014)","key":"11_CR20","DOI":"10.1109\/CVPRW.2014.131"},{"unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. CoRR abs\/1804.02767 (2018)","key":"11_CR21"},{"unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91\u201399 (2015)","key":"11_CR22"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1111\/j.1399-3054.2012.01599.x","volume":"146","author":"L Shabala","year":"2012","unstructured":"Shabala, L., et al.: Oxidative stress protection and stomatal patterning as components of salinity tolerance mechanism in quinoa (Chenopodium quinoa). Physiol. Plant. 146, 26\u201338 (2012)","journal-title":"Physiol. Plant."},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1093\/aob\/mct205","volume":"112","author":"S Shabala","year":"2013","unstructured":"Shabala, S.: Learning from halophytes: physiological basis and strategies to improve abiotic stress tolerance in crops. Ann. Bot. 112, 1209\u20131221 (2013)","journal-title":"Ann. Bot."},{"key":"11_CR25","first-page":"2044","volume":"180","author":"SS Shapiron","year":"1965","unstructured":"Shapiron, S.S., Wilk, M.B.: An analysis for variance test for normality (complete samples). Inf. Sci. 180, 2044\u20132064 (1965)","journal-title":"Inf. Sci."},{"key":"11_CR26","volume-title":"Handbook of Parametric and Nonparametric Statistical Procedures","author":"D Sheskin","year":"2011","unstructured":"Sheskin, D.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, London (2011)"},{"doi-asserted-by":"crossref","unstructured":"Simard, P., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the International Conference on Document Analysis and Recognition. ICDAR 2003, vol. 2, pp. 958\u2013964 (2003)","key":"11_CR27","DOI":"10.1109\/ICDAR.2003.1227801"},{"doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: Proceedings of 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2020 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","key":"11_CR28","DOI":"10.1109\/CVPR42600.2020.01079"},{"doi-asserted-by":"publisher","unstructured":"Tian, Y., et al.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput. Electron. Agric. 157, 417\u2013426 (2019). https:\/\/doi.org\/10.1016\/j.compag.2019.01.012","key":"11_CR29","DOI":"10.1016\/j.compag.2019.01.012"},{"doi-asserted-by":"crossref","unstructured":"Tian, Z., et al.: FCOS: fully convolutional one-stage object detection. CoRR abs\/1904.01355 (2019)","key":"11_CR30","DOI":"10.1109\/ICCV.2019.00972"},{"unstructured":"Tzutalin, D.: LabelImg (2015). https:\/\/github.com\/tzutalin\/labelImg","key":"11_CR31"},{"doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., Savvides, M.: Feature selective anchor-free module for single-shot object detection. CoRR abs\/1903.00621 (2019)","key":"11_CR32","DOI":"10.1109\/CVPR.2019.00093"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04881-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T22:51:59Z","timestamp":1727045519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04881-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031048807","9783031048814"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04881-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aveiro","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ibpria.org\/2022\/","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":"72","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":"54","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":"75% - 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)"}}]}}