Redes neurais artificiais na predição da produtividade de milho e definição de sítios de manejo diferenciado por meio de atributos do solo

Leal, Aguinaldo José Freitas; Miguel, Éder Pereira; Baio, Fábio Henrique Rojo; Neves, Danilo de Carvalho; Leal, Ulcilea Alves Severino

doi:10.1590/1678-4499.0140

Cited by 23 publications

(30 citation statements)

References 22 publications

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“…Promising results with the use of artificial neural networks were found by Leal et al (2015) adopting soil attributes in the simulation of corn grain yield. In study on adaptability and stability of cowpea, Teodoro et al (2015) found similarity of the parameters obtained by the traditional method of Eberhart & Russel (1966) and the use of artificial neural networks.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Simulation models are essential in the identification of factors that influence agricultural production and moreefficient managements (Mello & Caimi, 2008;Leal et al, 2015). Artificial intelligence (AI) techniques have emerged as an alternative in the development of simulation and optimization models (Leal et al, 2015;Soares et al, 2015). Among AI techniques, artificial neural networks (ANNs) present a mathematical model inspired in the neural structure of intelligent organisms, capable of performing computer learning and pattern recognition (McCulloch & Pitts, 1943;Çelebi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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A B S T R A C TArtificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m -2 ) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.Inteligência artificial na otimização da densidade de semeadura e simulação da produtividade da aveia R E S U M O O uso de inteligência artificial pode representar uma estratégia eficiente de simulação e otimização de processos importantes na agricultura. O objetivo deste estudo é propor o uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos, respectivamente, na simulação da produtividade de grãos de aveia (Avena sativa) e na otimização da densidade de semeadura, nos principais sistemas de sucessão do sul do Brasil. O estudo foi conduzido em blocos ao acaso com quatro repetições em esquema fatorial 4 x 2, para as densidades de semeadura (100, 300, 600 e 900 sementes m -2 ) e cultivares de aveia (Brisasul e URS Taura), nos sistemas de sucessão milho/aveia e soja/aveia. Implementou-se uma rede neural artificial de múltiplas camadas e um algoritmo genético, em linguagem de programação Java, e comparou-se os resultados obtidos desta implementação com análises tradicionais de regressão polinomial. O uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos permite simular com eficiência a produtividade de grãos de aveia e melhor otimização da densidade de semeadura na comparação com regressão polinomial, considerando os principais sistemas de sucessão no sul do Brasil.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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“…kg ha -1 ) were selected. These variables were normalized to equalize the ANN input data (Leal, Miguel, Baio, Neves, & Leal, 2015) so that the initial weights of the variables were assumed to be equivalent at the beginning of the training, thus avoiding the difficulties posed by variables with different weights that can prevent the ANN from converging.…”

Section: Methodsmentioning

confidence: 99%

<b>Estimating soybean yields with artificial neural networks

et al. 2018

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ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. Estimativa da produtividade de soja com redes neurais artificiais RESUMO. Para estimar a produtividade de muitas culturas, incluído a soja, são utilizados modelos estatísticos complexos, que torna restrito o acesso a essa prática. Uma alternativa a estes modelos é a utilização de sistemas computacionais empregando Redes Neurais Artificiais (RNA). Este trabalho teve por objetivo estimar a produtividade da soja baseada nos hábitos de crescimento, densidade de semeadura e características agronômicas usando RNA do tipo Multilayer Perceptron (MLP). Para isto foram utilizados dados agronômicos da cultura da soja obtidos em experimento conduzido na safra 2013/2014 em Anápolis, Estado de Goiás, Brasil, cujos dados foram normalizados em intervalo compatível para trabalho com RNA e em seguida feito o treinamento de várias RNAs para a escolha da RNA com melhor performance. Após o treinamento das redes, foi realizada a análise de performance para seleção da RNA com a performance mais adequada ao problema. A RNA selecionada apresentou um índice de acerto de 98% com os dados do treinamento e um acerto de 72% com dados de validação. A aplicação das RNAs do tipo MLP nos dados do experimento conduzido demonstram que é possível estimar a produtividade da soja baseando-se nas características agronômicas, hábito de crescimento e densidade populacional por meio da IA. Palavras-chave:Glycine max (L.) Merrill; características agronômicas; modelagem; MLP; perceptron.

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“…The data were divided into the different stages: 70% used for training and 30% for generalization (validation), and processed through software NeuroForest 3.3 (Binoti et al, 2014). The training algorithm used was Resilient Backpropagation, which, according to Leal et al (2015), it is the most skilled and recommended choice for Multilayer Perceptron ANN type.…”

Section: Training and Evaluation Of Annmentioning

confidence: 99%

Artificial Neural Networks For Predicting Soil Organic Carbon Stocks

2017

Aust. J. Basic & Appl. Sci.

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Redes neurais artificiais na predição da produtividade de milho e definição de sítios de manejo diferenciado por meio de atributos do solo

Cited by 23 publications

References 22 publications

Artificial intelligence in seeding density optimization and yield simulation for oat

Artificial intelligence in seeding density optimization and yield simulation for oat

<b>Estimating soybean yields with artificial neural networks

Artificial Neural Networks For Predicting Soil Organic Carbon Stocks

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