Predicting Aflatoxin Contamination in Peanuts: A Genetic Algorithm/Neural Network Approach

Aflatoxin contamination in peanut crops is a problem of significant health and financial importance. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the impact of a contaminated crop and is the goal of our research. Backpropagation neural networks have been used to model problems of this type, however development of networks poses the complex problem of setting values for architectural features and backpropagation parameters. Genetic algorithms have been used in other studies to determine parameters for backpropagation neural networks. This paper describes the development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data. Learning rate, momentum, and number of hidden nodes are the parameters that are set by the genetic algorithm. A three-layer feed-forward network with logistic activation functions is used. Inputs to the network are soil temperature, drought duration, crop age, and accumulated heat units. The project showed that the GA/BPN approach automatically finds highly fit parameter sets for backpropagation neural networks for the aflatoxin problem.

[1]  Tariq Samad,et al.  Towards the Genetic Synthesisof Neural Networks , 1989, ICGA.

[2]  R. J. Cole,et al.  Estimation of aflatoxin contamination in preharvest peanuts using neural networks , 1997 .

[3]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .

[4]  Larry D. Pyeatt,et al.  A comparison between cellular encoding and direct encoding for genetic neural networks , 1996 .

[5]  David J. Chalmers,et al.  The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .

[6]  Dipankar Dasgupta,et al.  Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms , 1998, Applied Intelligence.

[7]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[8]  Stephen G. Roberts,et al.  Evolving Neural Network Structures: An Evaluation of Encoding Techniques , 1995, ICANNGA.

[9]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[10]  Larry J. Eshelman,et al.  Using genetic search to exploit the emergent behavior of neural networks , 1990 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Vasant Honavar,et al.  Properties of Genetic Representations of Neural Architectures. , 1995 .

[13]  Timothy H. Sanders,et al.  Mean geocarposphere temperatures that induce preharvest aflatoxin contamination of peanuts under drought stress , 1985, Mycopathologia.

[14]  L. Marti.  Genetically generated neural networks. II. Searching for an optimal representation , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[15]  C. N. Thai,et al.  Relationship between aflatoxin production and soil temperature for peanuts under drought stress. , 1990 .

[16]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[17]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[18]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.