{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:16:40Z","timestamp":1743131800449,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031044465"},{"type":"electronic","value":"9783031044472"}],"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\/978-3-031-04447-2_23","type":"book-chapter","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T16:05:24Z","timestamp":1650557124000},"page":"340-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CoffeeSE: Interpretable Transfer Learning Method for\u00a0Estimating the\u00a0Severity of\u00a0Coffee Rust"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0474-4819","authenticated-orcid":false,"given":"Filomen","family":"Incahuanaco-Quispe","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0307-7567","authenticated-orcid":false,"given":"Edward","family":"Hinojosa-Cardenas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3913-6576","authenticated-orcid":false,"given":"Denis\u00a0A.","family":"Pilares-Figueroa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0173-4140","authenticated-orcid":false,"given":"Cesar A.","family":"Beltr\u00e1n-Casta\u00f1\u00f3n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,20]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1\u201374 (2021). https:\/\/doi.org\/10.1186\/s40537-021-00444-8","DOI":"10.1186\/s40537-021-00444-8"},{"key":"23_CR2","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s12571-015-0446-9","volume":"7","author":"J Avelino","year":"2015","unstructured":"Avelino, J., et al.: The coffee rust crises in Colombia and Central America (2008\u20132013): impacts, plausible causes and proposed solutions. Food Secur. 7, 303\u2013321 (2015)","journal-title":"Food Secur."},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Berns, R.: Color and Spatial Vision, pp. 17\u201335. Wiley, March 2019","DOI":"10.1002\/9781119367314.ch2"},{"issue":"9","key":"23_CR4","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","volume":"26","author":"Y Boykov","year":"2004","unstructured":"Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut\/max- flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124\u20131137 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Bridson, R.: Fast Poisson disk sampling in arbitrary dimensions. In: ACM SIGGRAPH 2007 Sketches on - SIGGRAPH 2007, pp. 22\u201332. ACM Press, San Diego (2007)","DOI":"10.1145\/1278780.1278807"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"C., R., Kolmogorov, V., Blake, A.: \u201cGrabCut\u201d: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309\u2013314 (2004)","DOI":"10.1145\/1015706.1015720"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., et al.: Interpretability of deep learning models: a survey of results. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation, pp. 1\u20136 (2017)","DOI":"10.1109\/UIC-ATC.2017.8397411"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Danahy, E.E., Agaian, S.S., Panetta, K.A.: Algorithms for the resizing of binary and grayscale images using a logical transform. In: Astola, J.T., Egiazarian, K.O., Dougherty, E.R. (eds.) Image Processing: Algorithms and Systems V, vol. 6497, pp. 305\u2013314. International Society for Optics and Photonics, SPIE (2007)","DOI":"10.1117\/12.704477"},{"key":"23_CR9","unstructured":"Deng, Y., Manjunath, B.S., Shin, H.: Color image segmentation. In: CVPR 1999, pp. 2446\u20132451 (1999)"},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3389\/fmed.2019.00264","volume":"6","author":"N Dimitriou","year":"2019","unstructured":"Dimitriou, N., Arandjelovi\u0107, O., Caie, P.D.: Deep learning for whole slide image analysis: an overview. Front. Med. 6, 264 (2019)","journal-title":"Front. Med."},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Doignon, C., Nageotte, F., de Mathelin, M.: Detection of grey regions in color images : application to the segmentation of a surgical instrument in robotized laparoscopy. In: 2004 IEEE\/RSJ International Conference on Intelligent Robots and Systems 2004. (IROS 2004). Proceedings, vol. 4, pp. 3394\u20133399, September 2004","DOI":"10.1109\/IROS.2004.1389941"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Esgario, J.G.M., Krohling, R.A., Ventura, J.A.: Deep learning for classification and severity estimation of coffee leaf biotic stress (2019)","DOI":"10.1016\/j.compag.2019.105162"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Esgario, J.G., de Castro, P.B., Tassis, L.M., Krohling, R.A.: An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning. Inf. Process. Agric. (2021)","DOI":"10.1016\/j.inpa.2021.01.004"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Esgario, J.G., Krohling, R.A., Ventura, J.A.: Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput. Electron. Agric. 169, 105162 (2020)","DOI":"10.1016\/j.compag.2019.105162"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Fuentes, A., Yoon, S., Kim, S., Park, D.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)","DOI":"10.3390\/s17092022"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Barbedo, J.G.A., et al.: Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Latin Am. Trans. 16(6), 1749\u20131757 (2018)","DOI":"10.1109\/TLA.2018.8444395"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning (2019)","DOI":"10.1109\/DSAA.2018.00018"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. JSTOR Appl. Stat. 28(1), 100\u2013108 (1979)","DOI":"10.2307\/2346830"},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Hitimana, E., Gwun, O.: Automatic estimation of live coffee leaf infection based on image processing techniques. Comput. Sci. Inf. Technol. (CS IT) (2014). https:\/\/doi.org\/10.5121\/csit.2014.4221","DOI":"10.5121\/csit.2014.4221"},{"key":"23_CR20","unstructured":"Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks (2019)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Jau, U.L., Teh, C.S., Ng, G.W.: A comparison of RGB and HSI color segmentation in real - time video images: a preliminary study on road sign detection. In: 2008 International Symposium on Information Technology, vol. 4, pp. 1\u20136 (2008)","DOI":"10.1109\/ITSIM.2008.4631913"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Khattab, D., Ebied, H., Hussein, A., Tolba, M.: Color image segmentation based on different color space models using automatic GrabCut. Sci. World J. 2014, 126025 (2014)","DOI":"10.1155\/2014\/126025"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Liu, B., Yin, C., Liu, Z., Zhang, Y.: Automatic segmentation on cell image fusing gray and gradient information. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2007. EMBS 2007, pp. 5624\u20135627, August 2007","DOI":"10.1109\/IEMBS.2007.4353622"},{"key":"23_CR24","doi-asserted-by":"publisher","unstructured":"Luo, M.R.: CIELAB. In: Luo, R. (ed.) Encyclopedia of Color Science and Technology, pp. 1\u20137. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-642-27851-8_11-1","DOI":"10.1007\/978-3-642-27851-8_11-1"},{"key":"23_CR25","unstructured":"Manso, G.L., Knidel, H., Krohling, R.A., Ventura, J.A.: A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust (2019)"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Marcos, A., Rodovalho, N.L.S., Backes, A.: Coffee leaf rust detection using convolutional neural network. In: 2019 XV Workshop de Vis\u00e3o Computacional (WVC), pp. 38\u201342 (2019)","DOI":"10.1109\/WVC.2019.8876931"},{"key":"23_CR27","doi-asserted-by":"publisher","unstructured":"Plataniotis, K., Venetsanopoulos, A.: Color Image Processing and Applications. Digital Signal Processing. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/978-3-662-04186-4","DOI":"10.1007\/978-3-662-04186-4"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Rahimzadeganasl, A., Sertel, E.: Automatic building detection based on CIE LUV color space using very high resolution pleiades images. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1\u20134 (2017)","DOI":"10.1109\/SIU.2017.7960711"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Rahman, M., Islam, M.: Segmentation of color image using adaptive thresholding and masking with watershed algorithm. In: 2013 International Conference on Informatics, Electronics Vision (ICIEV), pp. 1\u20136, May 2013","DOI":"10.1109\/ICIEV.2013.6572557"},{"key":"23_CR30","doi-asserted-by":"publisher","unstructured":"Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning, 1st edn. Springer, Boston (2011). https:\/\/doi.org\/10.1007\/978-0-387-30164-8","DOI":"10.1007\/978-0-387-30164-8"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Sanchez-Lopez, J.R., Marin-Hernandez, A., Palacios-Hernandez, E.R., Rios-Figueroa, H.V., Marin-Urias, L.F.: A real-time 3D pose based visual servoing implementation for an autonomous mobile robot manipulator. Procedia Technol. 7(0), 416\u2013423 (2013). 3rd Iberoamerican Conference on Electronics Engineering and Computer Science, CIIECC 2013","DOI":"10.1016\/j.protcy.2013.04.052"},{"key":"23_CR32","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv, June 2017"},{"issue":"8","key":"23_CR33","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s00138-013-0530-0","volume":"24","author":"JVB Soares","year":"2013","unstructured":"Soares, J.V.B., Jacobs, D.W.: Efficient segmentation of leaves in semi-controlled conditions. Mach. Vis. Appl. 24(8), 1623\u20131643 (2013). https:\/\/doi.org\/10.1007\/s00138-013-0530-0","journal-title":"Mach. Vis. Appl."},{"key":"23_CR34","unstructured":"S\u00f8renson, T.: A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons. Biologiske skrifter, I kommission hos E. Munksgaard (1948)"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Suhartono, D., Aditya, W., Lestari, M., Yasin, M.: Expert system in detecting coffee plant diseases. Int. J. Electr. Energy 156\u2013162 (2013)","DOI":"10.12720\/ijoee.1.3.156-162"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Talhinhas, P., et al.: The coffee leaf rust pathogen Hemileia vastatrix: one and a half centuries around the tropics. Mol. Plant Pathol. 18(8), 1039\u20131051 (2017)","DOI":"10.1111\/mpp.12512"},{"key":"23_CR38","doi-asserted-by":"publisher","unstructured":"Vezina, M., Ziou, D., Kerouh, F.: Color space identification for image display. In: Kamel, M., Campilho, A. (eds.) ICIAR 2015. LNCS, vol. 9164, pp. 465\u2013472. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20801-5_51","DOI":"10.1007\/978-3-319-20801-5_51"},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., H\u00e4nsch, R., Ma, L., Hellwich, O.: Comparison of different color spaces for image segmentation using graph-cut. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 301\u2013308 (2014)","DOI":"10.5220\/0004681603010308"},{"issue":"4","key":"23_CR40","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611\u2013629 (2018). https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Yebasse, M., Shimelis, B., Warku, H., Ko, J., Cheoi, K.J.: Coffee disease visualization and classification. Plants 10(6), 1257 (2021)","DOI":"10.3390\/plants10061257"},{"key":"23_CR42","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. CoRR abs\/1911.02685 (2019)"}],"container-title":["Communications in Computer and Information Science","Information Management and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04447-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T17:51:23Z","timestamp":1727027483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04447-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031044465","9783031044472"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04447-2_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMBig","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual International Conference on Information Management and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simbig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/simbig.org\/SIMBig2021\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","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":"25","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":"2","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":"37% - 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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}