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YIELD PREDICTION TECHNIQUE USING HYBRID ADAPTIVE NEURAL GENETIC NETWORK

KEFAYA QADDOUM

School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

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EVOR HINES

School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

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, and

DACIANA ILIESCU

School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

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https://doi.org/10.1142/S1469026812500216Cited by:3 (Source: Crossref)

Abstract

The application of neural networks (NNs) to problems of prediction has become increasingly popular. This paper presents a modified hybrid adaptive neural network with revised adaptive smoothing errors, based on a genetic algorithm, and using modified adaptive relaxation to build a learning system for complex problem solving in yield prediction. This system predicts weekly yield values of a tomato crop using environmental variables measured inside the greenhouse as inputs. The proposed learning system is an intelligent computing technique and the numerical values of the neural network connection weights are modified through a training algorithm, using a modified optimization approach. The paper further presents an analysis of the convergence rate of the error in a neural network. The method is evaluated using datasets from a tomato producer, so as to test the predictive ability of the method and compare it with standard models. The results show a comparatively good level of accuracy.

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