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A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.<\/p>","DOI":"10.4018\/ijmstr.2017040101","type":"journal-article","created":{"date-parts":[[2017,7,11]],"date-time":"2017-07-11T08:30:37Z","timestamp":1499761837000},"page":"1-14","source":"Crossref","is-referenced-by-count":4,"title":["Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders"],"prefix":"10.4018","volume":"5","author":[{"given":"Lucas Garcia","family":"Nachtigall","sequence":"first","affiliation":[{"name":"Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo Matsumura","family":"Araujo","sequence":"additional","affiliation":[{"name":"Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilmar Ribeiro","family":"Nachtigall","sequence":"additional","affiliation":[{"name":"Embrapa Grape & Wine, Vacaria, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJMSTR.2017040101-0","doi-asserted-by":"publisher","DOI":"10.5120\/2183-2754"},{"key":"IJMSTR.2017040101-1","doi-asserted-by":"publisher","DOI":"10.1109\/72.554190"},{"key":"IJMSTR.2017040101-2","unstructured":"Fialho, F., Garrido, L., Botton, M., de Melo, G., Fajardo, T., & Naves, R. 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