Título:	Use of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders.
Autoria:	NACHTIGALL, L. G. ARAUJO, R. M. NACHTIGALL, G. R.
Afiliação:	Lucas Garcia Nachtigall, Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil; Ricardo Matsumura Araujo, Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil; GILMAR RIBEIRO NACHTIGALL, CNPUV.
Ano de publicação:	2017
Referência:	International Journal of Monitoring and Surveillance Technologies Research, v. 5, n. 2, p. 1-14, April/June 2017.
Conteúdo:	Rapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. 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 resultsshowed 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. Keywords Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural Networks, Disorders, Machine Learning
Thesagro:	Doença Maçã Doença de planta
NAL Thesaurus:	Artificial Intelligence
Palavras-chave:	Macieira Apple Apple Disorders Automatic Disease Identification Classifications Convolutional Neural
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPUV)