Artificial Intelligence Simulating Grain Productivity During the Wheat Development Considering Biological And Environmental Indicators

Mamann, Ângela Teresinha Woschinski De; Silva, José Antônio Gonzalez da; Binelo, Manuel Osório; Scremin, Osmar Bruneslau; Kraisig, Adriana Roselia; Carvalho, Ivan Ricardo; Pereira, Laura Mensch; Berlezi, Júlio Daronco; Argenta, Cláudia Vanessa

doi:10.5296/jas.v7i3.15153

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Computational limitations until a few years ago meant that models with biological significance and statistical analyzes proved to be conflicting, leading to a preference for linear and/or polynomial models in relation to non-linear models, with greater biological significance. The computational advances already allow the use of nonlinear models of phenomena aggregating statistics and modeling together with biological processes, as well as the correct use of analysis of repeated measures in time, generating greater reliability for direct application in computer programs and applications in agriculture devices (Mamann, et al, 2019;Trautmann, et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Marolli¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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The internal points method (IPM-Carvalho), with regression analysis, can generate an efficient hybrid model for estimating oat grain productivity. We tested a combination of the internal points method and regression to estimate straw productivity. We also applied this methodology to forecast a harvest index in the elaboration of a hybrid model to estimate oat grain productivity, taking into account nitrogen management and growth regulator use, with biological and environmental indicators. Simulation of oat yield as a function of nitrogen and growth regulator applications, with biological and environmental inputs, can assist in the development of more efficient and sustainable management for this crop. Two experiments were conducted during 2013, 2014, and 2015; one was used to quantify biomass yield and the other to determine grain yield and plant lodging. The experimental design was randomized blocks with four replications in a 4 x 3 factorial scheme in the sources of variation, which were growth regulator (0, 200, 400 and 600 mL ha -1 ) and nitrogen (30, 90 and 150 kg ha applications. The environmental parameters that were included were rainfall and maximum air temperature. The nitrogen was applied as urea at the expanded fourth leaf stage. The growth regulator was trinexapac-ethyl applied at the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (2): gmr18756 A. Marolli et al. 2 stage between the 1st and 2nd visible stem node. Straw productivity was obtained by the IPM model with nitrogen dose and rainfall inputs. The harvest index was obtained by regression as a function of the growth regulator doses. The combination of the internal points method to estimate straw productivity with the use of regression in the forecast of the harvest index proved to be a useful model for estimating oat grain productivity based on biological and environmental parameters, together with nitrogen and growth regulator applications.

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Section: Resultsmentioning

confidence: 99%

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Marolli¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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“…Artificial neural networks (ANN) are among artificial intelligence techniques focused on implementing models that resemble biological neural structures (Scremin et al, 2020). Therefore, they can learn and generalize information from external data and provide consistent results for unknown data (Silva et al, 2018;De Mamann et al, 2019). Artificial neural network models can efficiently simulate and estimate results based on common characteristics of the selected input variables (Scremin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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Purpose: The objective of the study is to analyze the management of the fungicide that seeks a longer interval between the last application and the harvest of oat grains, with an indication of the cultivars with the highest satisfactory productivity without application in the grain filling. Validate artificial neural network models in the expectation of satisfactory productivity with food security, through the interaction between management, pathogen, genotype, and environment. Method/design/approach: The study was conducted in 2015, 2016, 2017, in a randomized block design, in a 22 x 4 factorial scheme, for 22 white oat cultivars (recommended and no longer present in the current Brazilian recommendation) and 4 fungicide application conditions (no application; one application at 60 days after emergence (DAE); two applications, 60 and 75 DAE; and three applications, at 60, 75, 90 DAE), with three repetitions. Results and conclusion: The oat cultivars URS Altiva, URS, Guará, URS Charrua, FAEM 4 Carlasul, IPR Aphrodite, and UPFPS Farroupilha showed satisfactory yields in absence of fungicide applications. URS Altiva, FAEM 4 Carlasul, and IPR Aphrodite showed significant yield increases with fungicide application before grain filling. Artificial neural networks are efficient in predicting productivity and are a reliable alternative for simulating scenarios to validate more sustainable management practices. Originality/value: An unprecedented study seeking more sustainable managements to reduce the use of fungicides in the control of oat diseases and development of simulation models by artificial intelligence, providing opportunities for analysis of different scenarios of the complex interaction plant, pathogen, and environment.

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“…High wheat yields depend on efficient cultivars, favorable soil and climate conditions for cultivation, and management technologies (Brezolin et al, 2017;Linina and Ruza, 2018). Among the management technologies, nitrogen fertilization is highlighted because nitrogen is directly linked to the processes of elaboration of grain yield components and is the nutrient most required and absorbed by plants (Zörb et al, 2018;De Mamann et al, 2019). The most used source of nitrogen in agriculture is urea, a soluble fertilizer that contains 45% nitrogen in its composition, with broadcast application on the soil (Theago et al, 2014;Santos et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The efficiency of nitrogen use by the urea source is affected by the type of residual cover and weather conditions during cultivation (Costa et al, 2017;Mantai et al, 2021). Under unfavorable conditions, nutrient losses by volatilization or leaching are imminent, limiting the expression of yield components and causing environmental pollution (Brezolin et al, 2016;De Mamann et al 2019). An alternative to reduce losses and damage to the ecosystem by nitrogen occurs with the management of the nutrient in fractioned doses, applied at different stages of wheat development, favoring the use of the nutrient and enhancing grain yield.…”

Section: Introductionmentioning

confidence: 99%

Research Article Dose and form of nitrogen supply in the relationship dynamics of wheat ear components with yield in cropping systems

Pansera,

Da Silva,

Alessi

et al. 2022

Genet. Mol. Res.

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Nitrogen management distinctly influences the expression and magnitude of relationships between wheat ear components and grain yield. We sought to determine the variables linked to wheat ear that are more responsive to changes in the dose and form of nitrogen supply and to determine the direct and indirect effects on grain yield by single and fractional nitrogen supply in soybean/wheat and corn/wheat succession systems. The study was conducted in 2018 and 2019 in a randomized block experimental design with four replications, in a 3 x 3 factorial, for N-fertilizer doses (30, 60, 120 kg ha-1) and forms of supply [single dose (100 %) in phenological stage V 3 (third expanded leaf); fractionated dose (70% and 30%) at the V 3 /V 6 phenological stage (third and sixth expanded leaf) and; fractionated dose (70% and 30%) at phenological stage V 3 /R 1 (expanded third leaf and beginning of grain filling)], respectively, in the soybean/wheat and corn/wheat succession systems. The increase in the nitrogen doses promotes productivity due to the greater contribution of grain mass and ear length in the V 3 stage and ear grain mass in V 3 /V 6 and V 3 /R 1, in a soybean/wheat system. In the corn/wheat system, the increase in nitrogen promotes productivity, with a greater contribution of grain ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 21 (1): gmr19008 V. Pansera et al. 2 mass in the ear, regardless of the form of supply. Nitrogen supply in single dose (V 3) and fractionated in theV 3 /V 6 gave similar productivity, with a reduction in V 3 /R 1. The grain mass of the ear showed greater contribution of alteration by the form of single and fractioned nitrogen supply, regardless of the dose and succession system. The grain mass of the ear shows a high correlation with yield, regardless of the dose and form of nitrogen supply in the soybean/wheat system, with a positive indirect effect by the ear mass. In the corn/wheat system, ear grain mass shows a high correlation with yield, when nitrogen is supplied in a single dose at 30 and 60 kg ha -1 , with a positive indirect effect due to ear length.

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Artificial Intelligence Simulating Grain Productivity During the Wheat Development Considering Biological And Environmental Indicators

Cited by 9 publications

References 26 publications

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Research Article Dose and form of nitrogen supply in the relationship dynamics of wheat ear components with yield in cropping systems

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