Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Silva, Aldo A. V. da; Silva, Inara A. F.; Filho, Marcelo Carvalho Minhoto Teixeira; Buzetti, Salatiér; Teixeira, Marcelo C. M.

doi:10.1590/s1415-43662014000200008

Cited by 20 publications

(20 citation statements)

References 22 publications

(15 reference statements)

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“…The use of artificial neural networks has presented itself as an efficient alternative to conventional models in the recognition of patterns and simulation of cultivation processes (Silva et al, 2014;Soares et al, 2014). 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.…”

Section: Resultsmentioning

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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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%

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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“…[4], contudo necessita de regras para produzir os resultados almejados. As regras devem ser elaboradas por especialistas que fornecem sua experiência profissional para a elaboração de um sistema de inferência [14]. Dessa forma a lógica fuzzy representa uma proposta importante e pode trazer grandes subsídiosà agricultura com a interpretação de dados linguísticos através de programas computacionais [11].…”

Section: Resultsunclassified

Lógica fuzzy na simulação da produtividade de trigo por nitrogênio e hidrogel

Mamann¹,

Brezolin²,

Scremin³

et al. 2018

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…Besides, combined simulation models allow us to analyze different scenarios, considering several factors that influence the productivity of each crop [41]. Thus, the integration of two or more models aims to obtain a more efficient model for the prediction of agricultural crops [42]. Reference [43] combined the expolinear-logistic model and the Gompertz model to estimate the variation of shoot dry matter accumulation in sugarcane cultivars.…”

Section: Resultsmentioning

confidence: 99%

“…Reference [43] combined the expolinear-logistic model and the Gompertz model to estimate the variation of shoot dry matter accumulation in sugarcane cultivars. Reference [42] combined models of Fuzzy Logic and Neural Networks to estimate wheat productivity as a function of nitrogen fertilization. Reference [41] using the combination of mathematical models were able to predict satisfactorily the grain productivity of the soybean crop, evidencing the best irrigation strategies that result in high grain productivity.…”

Section: Resultsmentioning

confidence: 99%

A Proposal of Oat Productivity Simulation by Meteorological Elements, Growth Regulator and Nitrogen

Marolli¹,

Silva²,

Scremin³

et al. 2017

AJPS

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The simulation of oat grain productivity does not contemplate the use of efficient models that involve important management with meteorological elements. The objective of the study is to propose a mathematical model capable of simulating the oat grain productivity through the management of nitrogen and growth regulator with variables related to the plant and to meteorological elements. In this study, two experiments were conducted in the years of 2013, 2014 and 2015: one to quantify biomass productivity and another to determine grain productivity and lodging at the management doses of nitrogen and growth regulator. The experimental design was a randomized block with four replications in a 4 × 3 factorial scheme for 0, 200, 400 and 600 mL•ha −1 growth regulator doses and 30, 90 and 150 kg•ha −1 nitrogen doses, respectively. During the crop cycles, the meteorological variables thermal sum, radiation and rainfall were quantified. The mathematical model proposed, which combines polynomial regression of the harvest index with multiple linear regression of the biological productivity, is efficient in the simulation of oat grains productivity with the use of growth regulator, nitrogen and meteorological elements. Thus, it adds to the conventional models of simulation and becomes an aid tool for making decisions regarding the management of oats culture.

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Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Cited by 20 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

Lógica fuzzy na simulação da produtividade de trigo por nitrogênio e hidrogel

A Proposal of Oat Productivity Simulation by Meteorological Elements, Growth Regulator and Nitrogen

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