Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean

Elizondo, David; McClendon, R. W.; Hoogenboom, Gerrit

doi:10.13031/2013.28168

Cited by 82 publications

(33 citation statements)

References 5 publications

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“…Based on the results of previous studies (NeuralWare 1995;Yang et al 1996a,b), a back-propagation network was selected to simulate the fluctuations of soil temperature because it has been applied successfully to modelling in many scientific and engineering fields, for example, to predict flowering and physiological maturity of soybean (Elizondo et al 1994) or to control the irrigation management of peanuts (McClendon et al 1996). The implementation of a back-propagation ANN requires a learning rule and a transfer function.…”

Section: Methodsmentioning

confidence: 99%

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An artificial neural network to estimate soil temperature

Yang¹,

Prasher²,

Mehuys³

1997

Can. J. Soil. Sci.

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An artificial neural network to estimate soil temperature. Can. J. Soil Sci. 77: 421-429. This study was undertaken to develop an artificial neural network (ANN) model for transient simulation of soil temperature at different depths in the profile. The capability of ANN models to simulate the variation of temperature in soils was investigated by considering readily available meteorologic parameters. The ANN model was constructed by using five years of meteorologic data, measured at a weather station at the Central Experimental Farm in Ottawa, Ontario, Canada. The model inputs consisted of daily rainfall, potential evapotranspiration, and the day of the year. The model outputs were daily soil temperatures at the depths of 100, 500 and 1500 mm. The estimated values were found to be close to the measured values, as shown by a root-mean-square error ranging from 0.59 to 1.82°C, a standard deviation of errors from 0.61 to 1.81°C, and a coefficient of determination from 0.937 to 0.987. Therefore, it is concluded that ANN models can be used to estimate soil temperature by considering routinely measured meteorologic parameters. In addition, the ANN model executes faster than a comparable conceptual simulation model by several orders of magnitude.

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Section: Methodsmentioning

confidence: 99%

“…For example, an ANN model has been used to predict soil moisture (Altendorf et al 1992). Elizondo et al (1994) developed ANN models to predict flowering and physiological maturity of soybean. McClendon et al (1996) applied ANNs to control and optimize the irrigation management of peanuts.…”

mentioning

confidence: 99%

An artificial neural network to estimate soil temperature

Yang¹,

Prasher²,

Mehuys³

1997

Can. J. Soil. Sci.

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“…The additional information about data like probability in probability theory, grade of membership in fuzzy set theory could also be discussed. The neural network is used in prediction of flowering and maturity dates of soybean [40] and in forecasting of water resources variables [41]. Veenadhari [42] studied the influence of climatic factors on major kharif and rabi crops production in Bhopal district of Madhya Pradesh state.…”

Section: Prediction Of Crop Yieldmentioning

confidence: 99%

Role of Data Mining Techniques in Agriculture Improvement

Sindhu¹,

Sindhu²

2017

International Journal of Advanced Research in Computer and Comm

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Data mining is the process of discovering and extracting of interesting patterns and knowledge from large amounts of data. The field of agriculture has to deal with large amounts of data and processing and retrieval of significant data from this abundance of agricultural information is necessary to help the farmers. Therefore, appropriate methods and techniques are required for managing and organizing this data to increase the efficiency and agricultural productivity. The application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel approach in agriculture. Data mining can help to process and convert this raw data into useful information for improving agriculture. In this paper, various data mining techniques used for processing of agricultural information/data such as k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described. With the advancement of novel and appropriate data mining techniques, different types of agricultural problems will be addressed to improve crop productivity.

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“…The neural network is used in Prediction of flowering and maturity dates of soybean [24] and in forecasting of water resources variables [25].…”

Section: The Applications Of Neural Network In the Field Of Agricultmentioning

confidence: 99%

A Brief survey of Data Mining Techniques Applied to Agricultural Data

Patel¹,

Patel²

2014

IJCA

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As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

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Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean

Cited by 82 publications

References 5 publications

An artificial neural network to estimate soil temperature

An artificial neural network to estimate soil temperature

Role of Data Mining Techniques in Agriculture Improvement

A Brief survey of Data Mining Techniques Applied to Agricultural Data

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