2017
DOI: 10.1590/1678-992x-2015-0309
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Abstract: Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications.In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding … Show more

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Cited by 20 publications
(15 citation statements)
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“…Supervised methods allow learning models for regression and classification using examples, i.e., images of healthy and diseased plants. In particular, artificial neural networks (ANNs) have been recently used to predict the Area Under the Disease Progress Curve (AUDPC) of tomato late blight infection [17]. ANNs have also been used in the past to automatically detect disease from spectral images of plants [18,19].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Supervised methods allow learning models for regression and classification using examples, i.e., images of healthy and diseased plants. In particular, artificial neural networks (ANNs) have been recently used to predict the Area Under the Disease Progress Curve (AUDPC) of tomato late blight infection [17]. ANNs have also been used in the past to automatically detect disease from spectral images of plants [18,19].…”
Section: Introductionmentioning
confidence: 99%
“…ANNs have also been used in the past to automatically detect disease from spectral images of plants [18,19]. ANNs can go beyond human capacity to evaluate large data banks and relate them to specific desirable characteristics [17].…”
Section: Introductionmentioning
confidence: 99%
“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”
Section: Introductionmentioning
confidence: 99%
“…In the agricultural sciences, several studies have used ANNs (Zhang, Bai, & Liu, 2007;Alvarez, 2009;Gago, Martínez-Núñez, Landín, & Gallego, 2010;Erzin, Rao, Patel, Gumaste, & Singh, 2010;Soares, Pasqual, Lacerda, Silva, & Donato, 2013;Safa, Samarasinghe, & Nejat, 2015;Alves et al, 2017).…”
Section: Introductionmentioning
confidence: 99%