{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:38:38Z","timestamp":1742913518979,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030608866"},{"type":"electronic","value":"9783030608873"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60887-3_29","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T23:04:50Z","timestamp":1602025490000},"page":"325-336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Rotten Fruit Detection Using a One Stage Object Detector"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2852-8387","authenticated-orcid":false,"given":"K.","family":"Perez-Daniel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-3817","authenticated-orcid":false,"given":"A.","family":"Fierro-Radilla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8766-7806","authenticated-orcid":false,"given":"J. P.","family":"Pe\u00f1aloza-Cobos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"issue":"7","key":"29_CR1","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1007\/s11947-011-0595-6","volume":"4","author":"I Arzate-V\u00e1zquez","year":"2011","unstructured":"Arzate-V\u00e1zquez, I., et al.: Image processing applied to classification of avocado variety hass (persea americana mill) during the ripening process. Food Bioprocess Technol. 4(7), 1307\u20131313 (2011)","journal-title":"Food Bioprocess Technol."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. Journal of King Saud University - Computer and Information Sciences, pp. 1\u201315 (2018)","DOI":"10.1016\/j.jksuci.2018.06.002"},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.neucom.2015.06.095","volume":"175","author":"H Calvo","year":"2016","unstructured":"Calvo, H., Moreno-Armend\u00e1riz, M.A., Godoy-Calder\u00f3n, S.: A practical framework for automatic food products classification using computer vision and inductive characterization. Neurocomputing 175, 911\u2013923 (2016)","journal-title":"Neurocomputing"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.biosystemseng.2017.04.009","volume":"159","author":"S C\u00e1rdenas-P\u00e9rez","year":"2017","unstructured":"C\u00e1rdenas-P\u00e9rez, S., et al.: Evaluation of the ripening stages of apple (golden delicious) by means of computer vision system. Biosyst. Eng. 159, 46\u201358 (2017)","journal-title":"Biosyst. Eng."},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.biosystemseng.2019.12.003","volume":"190","author":"AZ da Costa","year":"2020","unstructured":"da Costa, A.Z., Figueroa, H.E.H., Fracarolli, J.A.: Computer vision based detection of external defects on tomatoes using deep learning. Biosyst. Eng. 190, 131\u2013144 (2020)","journal-title":"Biosyst. Eng."},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"110102","DOI":"10.1016\/j.jfoodeng.2020.110102","volume":"286","author":"S Fan","year":"2020","unstructured":"Fan, S., et al.: On line detection of defective apples using computer vision system combined with deep learning methods. J. Food Eng. 286, 110102 (2020)","journal-title":"J. Food Eng."},{"issue":"2","key":"29_CR7","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1007\/s10462-019-09705-8","volume":"53","author":"L Goel","year":"2019","unstructured":"Goel, L., Raman, S., Dora, S.S., Bhutani, A., Aditya, A.S., Mehta, A.: Hybrid computational intelligence algorithms and their applications to detect food quality. Artif. Intell. Rev. 53(2), 1415\u20131440 (2019). \nhttps:\/\/doi.org\/10.1007\/s10462-019-09705-8","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"29_CR8","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.eswa.2011.07.073","volume":"39","author":"J G\u00f3mez-Sanchis","year":"2012","unstructured":"G\u00f3mez-Sanchis, J., Mart\u00edn-Guerrero, J.D., Soria-Olivas, E., Mart\u00ednez-Sober, M., Magdalena-Benedito, R., Blasco, J.: Detecting rottenness caused by penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst. Appl. 39(1), 780\u2013785 (2012)","journal-title":"Expert Syst. Appl."},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"281","DOI":"10.3390\/s19020281","volume":"19","author":"TM Hoang","year":"2019","unstructured":"Hoang, T.M., Nguyen, P.H., Truong, N.Q., Lee, Y.W., Park, K.R.: Deep retinanet-based detection and classification of road markings by visible light camera sensors. Sensors (Basel, Switz.) 19, 281 (2019)","journal-title":"Sensors (Basel, Switz.)"},{"key":"29_CR12","unstructured":"ITU: H.264 : Advanced video coding for generic audiovisual services (2018). urlhttps:\/\/www.itu.int\/rec\/T-REC-H.264-201906-I\/en"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao, L., et al.: A survey of deep learning-based object detection. IEEE Access 7, 128837\u2013128868 (2019)","journal-title":"IEEE Access"},{"key":"29_CR14","unstructured":"Kalluri, S.R.: Fruits: fresh and rotten for classification Dataset (2018). urlhttps:\/\/www.kaggle.com\/sriramr\/fruits-fresh-and-rotten-for-classification"},{"issue":"2","key":"29_CR15","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2020","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). \nhttps:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: SSD: Single shot multibox detector. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"03","key":"29_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3991\/ijoe.v15i03.9832","volume":"15","author":"A Nosseir","year":"2019","unstructured":"Nosseir, A., Ahmed, S.E.A.: Automatic classification for fruits\u2019 types and identification of rotten ones using k-nn and svm. Int. J. Online Biomed. Eng. 15(03), 47\u201361 (2019)","journal-title":"Int. J. Online Biomed. Eng."},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"6","key":"29_CR20","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"29_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015). \nhttps:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"key":"29_CR22","doi-asserted-by":"publisher","first-page":"12489","DOI":"10.3390\/s120912489","volume":"12","author":"Y Zhang","year":"2012","unstructured":"Zhang, Y., Wu, L.: Classification of fruits using computer vision and a multiclass support vector machine. Sensors (Basel, Switz.) 12, 12489\u201312505 (2012)","journal-title":"Sensors (Basel, Switz.)"},{"issue":"1","key":"29_CR23","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1080\/10942912.2019.1669638","volume":"22","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Li, G.: Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. Int. J. Food Prop. 22(1), 1709\u20131719 (2019)","journal-title":"Int. J. Food Prop."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60887-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T23:14:50Z","timestamp":1602026090000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60887-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030608866","9783030608873"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60887-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2020\/","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":"186","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":"77","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":"41% - 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)"}}]}}