{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:41:24Z","timestamp":1763664084971},"publisher-location":"Cham","reference-count":51,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030302405"},{"type":"electronic","value":"9783030302412"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-30241-2_23","type":"book-chapter","created":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T09:56:10Z","timestamp":1567245370000},"page":"258-271","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Digital Ampelographer: A CNN Based Preliminary Approach"],"prefix":"10.1007","author":[{"given":"Telmo","family":"Ad\u00e3o","sequence":"first","affiliation":[]},{"given":"Tatiana M.","family":"Pinho","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Joaquim J.","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[]},{"given":"Raul","family":"Morais","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/B978-0-12-815260-7.00012-2","volume-title":"Production and Management of Beverages","author":"Elisa Giacosa","year":"2019","unstructured":"Giacosa, E.: Wine consumption in a certain territory. Which factors may have impact on it? In: Grumezescu, A.M., Holban, A.M. (eds.) Production and Management of Beverages, pp. 361\u2013380. Woodhead Publishing (2019). https:\/\/doi.org\/10.1016\/B978-0-12-815260-7.00012-2"},{"key":"23_CR2","unstructured":"OIV: State of the Vitiviniculture World Market: State of the sector in 2018. Organisation Internationale de la Vigne et du Vin (OIV) (2019)"},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.foodchem.2015.03.081","volume":"184","author":"Silvana M. Azcarate","year":"2015","unstructured":"Azcarate, S.M., et al.: Modeling excitation\u2013emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety. Food Chem. 184, 214\u2013219 (2015). https:\/\/doi.org\/10.1016\/j.foodchem.2015.03.081","journal-title":"Food Chemistry"},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.talanta.2016.05.059","volume":"158","author":"S Moncayo","year":"2016","unstructured":"Moncayo, S., Rosales, J.D., Izquierdo-Hornillos, R., Anzano, J., Caceres, J.O.: Classification of red wine based on its protected designation of origin (PDO) using laser-induced breakdown spectroscopy (LIBS). Talanta 158, 185\u2013191 (2016). https:\/\/doi.org\/10.1016\/j.talanta.2016.05.059","journal-title":"Talanta"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Rinaldi, A.: Wine global trends. Traditional leaders and new markets. Rivista di Scienze del Turismo - Ambiente Cultura Diritto Economia 6, 5\u201310 (2018). https:\/\/doi.org\/10.7358\/rst-2015-01-rina","DOI":"10.7358\/rst-2015-01-rina"},{"key":"23_CR6","unstructured":"Hogg, T., Rebelo, J.: Rumo Estrat\u00e9gico para o Setor dos Vinhos do Porto e Douro, Relat\u00f3rio Final - Estudos de base, Instituto dos Vinhos do Douro e do Porto, I.P. Universidade de Tr\u00e1s-os-Montes e Alto Douro, INNOVINE&WINE, Vila Real, Portugal (2014)"},{"key":"23_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1080\/09571260902978527","volume":"20","author":"LA Panzone","year":"2009","unstructured":"Panzone, L.A., Sim\u00f5es, O.M.: The importance of regional and local origin in the choice of wine: hedonic models of Portuguese wines in Portugal. J. Wine Res. 20, 27\u201344 (2009). https:\/\/doi.org\/10.1080\/09571260902978527","journal-title":"J. Wine Res."},{"key":"23_CR8","unstructured":"Di\u00e1rio da Rep\u00fablica: Decreto Lei no 173\/2009 de 3 de Agosto (2009)"},{"key":"23_CR9","doi-asserted-by":"publisher","first-page":"8643","DOI":"10.1007\/s00216-016-9841-0","volume":"408","author":"FJ Gomez","year":"2016","unstructured":"Gomez, F.J., Silva, M.F.: Microchip electrophoresis for wine analysis. Anal. Bioanal. Chem. 408, 8643\u20138653 (2016). https:\/\/doi.org\/10.1007\/s00216-016-9841-0","journal-title":"Anal. Bioanal. Chem."},{"key":"23_CR10","unstructured":"Tassie, L.: Vine identification \u2013 knowing what you have (2010)"},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Garcia-Mu\u00f1oz, S., Mu\u00f1oz-Organero, G., Andr\u00e9s, M.T. de, Cabello, F.: Ampelography - an old technique with future uses: the case of minor varieties of Vitis vinifera L. from the Balearic Islands. OENO One 45, 125\u2013137 (2011). https:\/\/doi.org\/10.20870\/oeno-one.2011.45.3.1497","DOI":"10.20870\/oeno-one.2011.45.3.1497"},{"key":"23_CR12","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris, A., Prenafeta-Bold\u00fa, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70\u201390 (2018). https:\/\/doi.org\/10.1016\/j.compag.2018.02.016","journal-title":"Comput. Electron. Agric."},{"key":"23_CR13","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [cs] (2014)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 [cs] (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261 [cs] (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv:1610.02357 [cs] (2016)","DOI":"10.1109\/CVPR.2017.195"},{"key":"23_CR17","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 [cs] (2017)"},{"key":"23_CR18","unstructured":"Di\u00e1rio da Rep\u00fablica: Portaria n.o 383\/2017, de 20 de dezembro (2017)"},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat, G.L., Uzal, L.C., Larese, M.G., Granitto, P.M.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418\u2013424 (2016). https:\/\/doi.org\/10.1016\/j.compag.2016.07.003","journal-title":"Comput. Electron. Agric."},{"key":"23_CR20","doi-asserted-by":"publisher","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 (2015). https:\/\/doi.org\/10.1109\/ICIP.2015.7350839","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Hall, D., McCool, C., Dayoub, F., Sunderhauf, N., Upcroft, B.: Evaluation of features for leaf classification in challenging conditions. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 797\u2013804 (2015). https:\/\/doi.org\/10.1109\/WACV.2015.111","DOI":"10.1109\/WACV.2015.111"},{"key":"23_CR22","unstructured":"Reyes, A.K., Caicedo, J.C., Camargo, J.E.: Fine-tuning deep convolutional networks for plant recognition. In: CLEF (Working Notes), vol. 1391, p. 9 (2015)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Pound, M.P., et al.: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience 6, 1\u201310 (2017). https:\/\/doi.org\/10.1093\/gigascience\/gix083","DOI":"10.1093\/gigascience\/gix083"},{"key":"23_CR24","unstructured":"Mortensen, A.K., Dyrmann, M., Karstoft, H., J\u00f8rgensen, R.N., Gislum, R.: Semantic segmentation of mixed crops using deep convolutional neural network. In: CIGR-AgEng Conference, Aarhus, Denmark, 26\u201329 June 2016, Abstracts and Full papers, pp. 1\u20136 (2016)"},{"key":"23_CR25","doi-asserted-by":"publisher","first-page":"29779","DOI":"10.1007\/s11042-017-5578-9","volume":"77","author":"H Zhu","year":"2018","unstructured":"Zhu, H., Liu, Q., Qi, Y., Huang, X., Jiang, F., Zhang, S.: Plant identification based on very deep convolutional neural networks. Multimed. Tools Appl. 77, 29779\u201329797 (2018). https:\/\/doi.org\/10.1007\/s11042-017-5578-9","journal-title":"Multimed. Tools Appl."},{"key":"23_CR26","doi-asserted-by":"publisher","unstructured":"Lee, J.W., Yoon, Y.C.: Fine-grained plant identification using wide and deep learning model 1. In: 2019 International Conference on Platform Technology and Service (PlatCon), pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/PlatCon.2019.8669407","DOI":"10.1109\/PlatCon.2019.8669407"},{"key":"23_CR27","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14, 778\u2013782 (2017). https:\/\/doi.org\/10.1109\/LGRS.2017.2681128","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23_CR28","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3390\/ijgi7040129","volume":"7","author":"M Ru\u00dfwurm","year":"2018","unstructured":"Ru\u00dfwurm, M., K\u00f6rner, M.: Multi-temporal land cover classification with sequential recurrent encoders. ISPRS Int. J. Geo-Inf. 7, 129 (2018). https:\/\/doi.org\/10.3390\/ijgi7040129","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"23_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3289801","volume":"2016","author":"Srdjan Sladojevic","year":"2016","unstructured":"Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. https:\/\/www.hindawi.com\/journals\/cin\/2016\/3289801\/ . https:\/\/doi.org\/10.1155\/2016\/3289801","journal-title":"Computational Intelligence and Neuroscience"},{"key":"23_CR30","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty, S.P., Hughes, D.P., Salath\u00e9, M.: Using deep learning for image-based plant disease detection. Front Plant Sci. 7, 1419 (2016). https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci."},{"key":"23_CR31","unstructured":"Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. In: BTW (2017)"},{"key":"23_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.34133\/2019\/9237136","volume":"2019","author":"Yosuke Toda","year":"2019","unstructured":"Toda, Y., Okura, F.: How convolutional neural networks diagnose plant disease (2019). https:\/\/spj.sciencemag.org\/plantphenomics\/2019\/9237136\/ . https:\/\/doi.org\/10.1155\/2019\/9237136","journal-title":"Plant Phenomics"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. arXiv:1409.4842 [cs] (2014)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"23_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23 . arXiv:1406.4729 [cs]"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv:1512.00567 [cs] (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. arXiv:1801.04381 [cs] (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"23_CR37","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. arXiv:1409.0575 [cs] (2014)"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"23_CR39","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1214\/aoms\/1177729392","volume":"23","author":"J Kiefer","year":"1952","unstructured":"Kiefer, J., Wolfowitz, J.: Stochastic estimation of the maximum of a regression function. Ann. Math. Statist. 23, 462\u2013466 (1952). https:\/\/doi.org\/10.1214\/aoms\/1177729392","journal-title":"Ann. Math. Statist."},{"key":"23_CR40","first-page":"543","volume":"269","author":"YE Nesterov","year":"1983","unstructured":"Nesterov, Y.E.: A method for solving the convex programming problem with convergence rate O(1\/k^2). Dokl. Akad. Nauk SSSR 269, 543\u2013547 (1983)","journal-title":"Dokl. Akad. Nauk SSSR"},{"key":"23_CR41","unstructured":"Brownlee, J.: Deep learning with python: develop deep learning models on Theano and TensorFlow Using Keras, Melbourne, Australia"},{"key":"23_CR42","unstructured":"Tieleman, T., Hinton, G.: Lecture 6.5 - RMSProp: divide the gradient by a running average of its recent magnitude (2012)"},{"key":"23_CR43","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2014)"},{"key":"23_CR44","unstructured":"Dozat, T.: Incorporating Nesterov Momentum into Adam (2016)"},{"key":"23_CR45","unstructured":"TensorFlow. https:\/\/www.tensorflow.org\/"},{"key":"23_CR46","unstructured":"Welcome \u2014 Theano 0.9.0 documentation. http:\/\/deeplearning.net\/software\/theano\/"},{"key":"23_CR47","unstructured":"Keras Documentation. https:\/\/keras.io\/"},{"key":"23_CR48","unstructured":"Deep Learning Toolbox. https:\/\/www.mathworks.com\/products\/deep-learning.html"},{"key":"23_CR49","unstructured":"Caffe|Deep L. Framework. https:\/\/caffe.berkeleyvision.org\/"},{"key":"23_CR50","unstructured":"PyTorch. https:\/\/www.pytorch.org"},{"key":"23_CR51","unstructured":"Deep Cognition. https:\/\/deepcognition.ai\/"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30241-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T02:04:21Z","timestamp":1664244261000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30241-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030302405","9783030302412"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30241-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vila Real","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","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":"epia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2019.utad.pt\/","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":"252","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":"119","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":"6","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":"47% - 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":"3.32","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":"1.86","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)"}}]}}