Neural Network Model for the Evaluation of Lettuce Plant Growth

Zaidi, M. A.; Murase, Hiroshi; Honami, N.

doi:10.1006/jaer.1999.0452

Cited by 25 publications

(9 citation statements)

References 13 publications

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“…Moreover, many biological variables that influence insect populations, such as natural enemies or host plants, are also affected by the weather (Worner, 1998). However, because of the non‐linear and complex nature of the population dynamics of insects in relation to their environment, it is often very difficult to reliably predict future population densities using traditional modeling methods (Allen, 1992; De Wolf & Francl, 1997; Zaidi et al., 1999; Chon et al., 2000). Complex simulation models often require data that are not commonly available, whereas standard statistical approaches are subject to over‐fitting and often do not generalize well to new data.…”

Section: Introductionmentioning

confidence: 99%

“…Drechsler, in outdoor environments based on climatic variables. Zaidi et al. (1999) evaluated the use of ANN models to simulate lettuce ( Lactuca sativa L.) plant growth.…”

Section: Introductionmentioning

confidence: 99%

“…Drechsler, in outdoor environments based on climatic variables. Zaidi et al (1999) evaluated the use of ANN models to simulate lettuce ( Lactuca sativa L.) plant growth. Gevrey & Worner (2006) have used back propagation neural network models to predict insect establishment.…”

Section: Introductionmentioning

confidence: 99%

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An ensemble model for predicting Rhopalosiphum padi abundance

Lankin-Vega

Worner

Teulon

2008

Entomologia Exp Applicata

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A novel modeling method is proposed to predict the abundance of the main vector of barley yellow dwarf virus in autumn sown cereal crops. An ensemble model based on artificial neural networks (ANN) was developed to predict the number of Rhopalosiphum padi (L.) (Homoptera: Aphididae) caught in traps during the autumn flight period at Lincoln, Canterbury, New Zealand, over the period 1982–2003. Artificial neural networks were trained using historical weather data and aphid data collected from a suction trap. Model results were compared with those obtained using multiple regression (MR) models using the same independent variables. Both ANN and MR models were validated by leave‐one‐out validation, in other words, by sequentially jackknifing each year out of the data set, fitting a model to the remaining data, then using that model to predict the number of aphids for each jackknifed year. A linear ensemble of ANN models further improved the predictions and represented the trends in the number of aphids over the 22‐year period very well. The r2 between the predicted and observed numbers of aphids for the ANN models changed from 0.68 to 0.83 using the linear ensemble model, but the ensemble approach did not change the prediction for the MR models. The absolute mean error (ABSME) of prediction was much lower for the ANN ensemble predictions compared to that for the MR models. The ABMSE for the ANN models dropped from 109 to 86 aphids compared to an ABMSE reduction from 245 to 220 aphids for the MR models. We discuss the potential for ensemble models for predicting insect abundance when long‐term historical data are available.

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Section: Introductionmentioning

confidence: 99%

“…Drechsler, in outdoor environments based on climatic variables. Zaidi et al. (1999) evaluated the use of ANN models to simulate lettuce ( Lactuca sativa L.) plant growth.…”

Section: Introductionmentioning

confidence: 99%

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An ensemble model for predicting Rhopalosiphum padi abundance

Lankin-Vega

Worner

Teulon

2008

Entomologia Exp Applicata

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“…1 The location of shrimp farming Iran about the models and seem to be suited for predicting growth of living organisms. For example, ANNs have been applied to prediction of microbiology and bacterial growth (Hajmeer et al 2000;Lou and Nakai 2001;Garci´a-Gimeno et al 2002, 2005Panagou et al 2007), plant growth (Zaidi et al 1999;Zhang et al 2007), and animal growth (Roush et al 2006). Only a few studies have used these models to predict shrimp growth.…”

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confidence: 98%

Prediction of shrimp growth using an artificial neural network and regression models

Esmaeili

Tarazkar

2010

Aquacult Int

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Uncertainty and lack of information about the future make it difficult for shrimp farmers to develop and plan harvesting schedules. To do this effectively, farmers should be able to predict shrimp growth. A reliable prediction of growth and survival would also give farmers a better insight into future productivity and profitability. Linear and nonlinear regression models have been used to estimate growth of different types of animals. These models include theoretic guesses and hypotheses about the underlying laws that govern the system from which data are generated. Compared to such models, artificial neural networks (ANNs) make a few priori assumptions about the models and suited for predicting animal growth. This study evaluated the potential of an ANN as an alternative to regressions models for predicting shrimp growth. Empirical data were collected from 9 commercial shrimp farms in the Bushehr Province of Iran. The results showed that the ANN performed better compared to linear and nonlinear regression models for predicting the growth of farmed shrimp.

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“…To estimate evapotranspiration Input Data of meteorological variables and physical basis involved in the evapotranspiration process and the estimates provided by empirical models Alg. (Zaidi et al, 1999) Prob. Neural networks showed their potential for modeling evapotranspiration Ref.…”

Section: Evaporationmentioning

confidence: 99%