Predição da produtividade da cultura do milho utilizando rede neural artificial

Soares, Fátima Cibele; Robaina, Adroaldo Dias; Peiter, Márcia Xavier; Russi, Jumar Luís

doi:10.1590/0103-8478cr20141524

Cited by 26 publications

(30 citation statements)

References 14 publications

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“…Rogenski et al (2012) found efficiency of the artificial neural networks in the estimation of infection percentage of leaf diseases in wheat, as assistance in decision-making. Soares et al (2015) observed the possibility of using artificial neural networks in the estimation of corn grain yield, considering the morphological variables of the crop. The efficiency of artificial neural networks in the processes of simulation was also observed by Soares et al (2014) in the estimation of bean yield.…”

Section: Resultsmentioning

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%

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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“…Artificial neural networks (ANNs) are indicated to deal with complex systems (Jana et al, 2012;Soares et al, 2015). For this study, the Kohonem Map was employed using 36 entries (neurons) represented by the dual-purpose wheat characters.…”

Section: Definition Of Inheritable Profiles Through Artificial Neuralmentioning

confidence: 99%

Research Article Heritability profiles defined by hierarchal models and artificial neural networks for dual-purpose wheat attributes

Carvalho¹,

Silva²,

Ferreira³

et al. 2019

Genet. Mol. Res.

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Dual purpose wheat could be a good alternative for helping overcome the need to import this cereal in Brazil. To achieve this, development of cultivars with high yield is necessary. The contribution of genetics in defining traits is very important for directing breeding programs for the development of cultivars that provide the desired agronomic ideotype. We estimated heritability for 36 characters of agronomic importance in dual-purpose wheat. The inheritable genetic patterns were examined using linear trends, a Euclidean algorithm, factor analysis and artificial neural networks. The study was carried out during the crop seasons of 2011, 2012 and 2013. The experimental design was randomized block, arranged in a factorial scheme with three growing seasons (2011, 2012 and 2013) and five dual-purpose wheat genotypes (BRS Tarumã, BRS Umbu, BRS Figueira, BRS Guatambu and BRS 277) x three cuttings (first cutting, second cutting and third cutting), with three replicates. Deviance analysis or maximum likelihood was significant for the 36 characters. The length of the head of the main plant, plant height ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (3): gmr18266 I.R. Carvalho et al. 2 before the first second cutting and dry mass of the seedlings showed high variability. The 36 characters expressed linear genetic dependence based on the Euclidean Algorithm; similar to what was found with the Tocher Optimized Clustering and Artificial Neural Networks K-means methods. Similar genetic trends for heritability profiles were obtained with factor analysis and Artificial Neural Networks by the Kohonem method. The use of Artificial Neural Networks through the Kohonem method gave the greatest efficacy in the definition of the genetic profiles needed to develop the recommended agronomic ideotype for the improvement of dualpurpose wheat.

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“…For their part, artificial neural networks are computational techniques inspired by the neural architecture of the human brain, which acquires knowledge through experience (Braga et al 2012). Thus, it is able to recognize patterns, i.e., it has the ability to learn through examples and generalize information learned, generating a non-linear model (Soares et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Lack of information combined with fierce competition to obtain resources for research justify the use of mathematical modeling to predict yield. For Soares et al (2015), prominent advantages of use of models are savings in time, work, and volume of resources for planning and decision-making in the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Sousa

Silva

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

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The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and São Domingos do Maranhão, MA, through measurement of 12 traits, except for grain yield in São Domingos do Maranhão. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.

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Predição da produtividade da cultura do milho utilizando rede neural artificial

Cited by 26 publications

References 14 publications

Artificial intelligence in seeding density optimization and yield simulation for oat

Artificial intelligence in seeding density optimization and yield simulation for oat

Research Article Heritability profiles defined by hierarchal models and artificial neural networks for dual-purpose wheat attributes

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

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