2019
DOI: 10.1016/j.scienta.2019.108724
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Artificial neural network modelling in the prediction of bananas’ harvest

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Cited by 26 publications
(21 citation statements)
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References 13 publications
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“…In a complementary study using the modeling of artificial neural networks to evaluate banana gestation data, the authors verified that this model presented unprecedented results, with a low error in the average gestation time of the bunch, thus becoming a tool for producers to manage their production. This complementary study obtained a good performance in the training process (Wi weights all adjusted) with an error (MSE) of 0.00397 and an R value of 0.8998, resulting in a strongly positive linear correlation (Souza et al, 2019).…”
Section: Resultsmentioning
confidence: 83%
“…In a complementary study using the modeling of artificial neural networks to evaluate banana gestation data, the authors verified that this model presented unprecedented results, with a low error in the average gestation time of the bunch, thus becoming a tool for producers to manage their production. This complementary study obtained a good performance in the training process (Wi weights all adjusted) with an error (MSE) of 0.00397 and an R value of 0.8998, resulting in a strongly positive linear correlation (Souza et al, 2019).…”
Section: Resultsmentioning
confidence: 83%
“…Bananas are a popularly marketed fresh fruit grown in more than 120 countries around the world [1]. The global production of bananas in 2017 is about 113 million tons [2] and is of significant economic and social value.…”
Section: Introductionmentioning
confidence: 99%
“…Muitos estudos têm sido reportados sobre o uso de inteligência artificial na agricultura (Bala et al, 2005;Diamantopoulou, 2005;Movagharnejad & Nikzad, 2007;Zhang et al, 2007), sendo muitos destes estudos dedicados a predições (Jiang et al, 2004;Uno et al, 2005;Savin et al, 2007;De Souza et al 2019). No Brasil, alguns trabalhos têm sido desenvolvidos na área da ciência do solo.…”
Section: Introductionunclassified
“…Foi proposto o desenvolvimento de uma RNA com o objetivo de analisar dados de solos em recuperação de forma que se possa classificá-los automaticamente em função de seus atributos físicos. Em De Souza et al 2019 foi proposto uma RNA para estimar os dias da colheita dos cachos da banana em função dos dados meteorológicos. O erro médio na fase de operação da rede foi em torno de 0,0596, ou seja, 6% de erro em comparação da saída obtida em relação à saída desejada.…”
Section: Introductionunclassified