Development of a Neural Network for Soybean Rust Epidemics

Batchelor, William D.; Yang, Xiaoli; Tschanz, A. T.

doi:10.13031/2013.21237

Cited by 64 publications

(47 citation statements)

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“…The performance of ANN in the study was mainly attributed to the ability of ANNs to capture the nonlinear input-output relationship between crop growth and soil moisture and salinity, whereas MLRs were unable to reflect these complicated relationships due to their linear characteristics. Batchelor et al (1997) showed that the ANN had the advantage over other empirical modeling techniques that do not assume a priory structure for the data, are well suited for fitting non-linear relationships and complex interactions, and can expose hidden relationships among input variables.…”

Section: Discussionmentioning

confidence: 99%

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

Dai

Huo

Wang

2011

Field Crops Research

117

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Section: Discussionmentioning

confidence: 99%

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

Dai

Huo

Wang

2011

Field Crops Research

117

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“…Starret et al (1997) reported that models of ANN had better performance (r 2 = 0.984) than the regression model (r 2 = 0.780) for N used in gram. According to Batchelor et al (1997), models of ANN achieve better results when compared to traditional statistical methods for prediction of soybean rust.…”

Section: It Is Found Inmentioning

confidence: 99%

Application of neural networks to predict volume in eucalyptus

Bhering

Cruz

Rosado

et al. 2015

Crop Breed. Appl. Biotechnol.

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-The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models. Keywords

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“…Neural network (NN), also known as artificial neural network (ANN), analysis is a powerful tool for data modeling (Batchelor et al 1997;Kachrimanis et al 2003;Baawain et al 2007;Marini et al 2007;Ochoa-Martinez et al 2007;Sofu and Ekinci 2007;Olajos et al 2008;Riahi et al 2008). NN is a type of computeralgorithm architecture that is able to relate inputs and outputs through training, or of learning through iteration, even though no prior knowledge about the relationships between input and output parameters exists.…”

Section: Artificial Neural Network Modeling Of Distillers Dried Grainmentioning

confidence: 99%

Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

2011

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Cereal Chem. 88(5):480-489Neural network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying levels of condensed distillers solubles (10, 15, and 20%, wb), drying temperatures (100, 200, and 300°C), cooling temperatures (-12, 25, and 35°C), and storage times (0 and 1 month). Response variables were selected based on our previous research results and included aerated bulk density, Hausner ratio, angle of repose, total flowability index, and Jenike flow index. Various NN models were developed using multiple input variables in order to predict single-response and multiple-response variables simultaneously. The NN models were compared based on R 2 , mean square error, and coefficient of variation obtained. In order to achieve results with higher R 2 and lower error, the number of neurons in each hidden layer, the step size, the momentum learning rate, and the number of hidden layers were varied. Results indicate that for all the response variables, R 2 > 0.83 was obtained from NN modeling. Compared with our previous studies, NN modeling provided better results than either partial least squares modeling or regression modeling, indicating greater robustness in the NN models. Surface plots based on the predicted values from the NN models yielded process and storage conditions for favorable versus cohesive flow behavior for DDGS. Modeling of DDGS flowability using NN has not been done before, so this work will be a step toward the application of intelligent modeling procedures to this industrial challenge.

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Development of a Neural Network for Soybean Rust Epidemics

Cited by 64 publications

References 0 publications

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

Application of neural networks to predict volume in eucalyptus

Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

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