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Authors
Affiliations
1 Dept. of Computer Science, Shrimathi Indira Gandhi College, Tiruchirappalli 620 002, Tamil Nadu., IN
2 Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu., IN
3 Department of Entomology, University of Agricultural Science, Raichur 584 102, Karnataka, IN
4 Indian Institute of Horticultural Research, Hessaraghatta Lake Post, Bangalore 560 089, Karnataka, IN
Source
Journal of Biological Control, Vol 25, No 2 (2011), Pagination: 134-142
Abstract
The cotton bollworm, Helicoverpa armigera (Hiibner) is an important pest in India damaging cotton crop and resulting in economic loss. Accurate and timely prediction of the pest, considering biotic and abiotic factors is essential to reduce the crop loss. In this paper, we present a neural-network classifier for predicting the pest incidence on cotton by considering the season, crop phenology, biotic factors (spiders and Chrysoperla zastrowi sillemi) and abiotic factors such as maximum temperature, minimum temperature, rainfall and relative humidity. Single layer perceptron neural-network with back-propagation algorithm was utilized for the design of the presented intelligent system. Decision tree is presented from the proposed trained neural-network. The results showed that the supervised neural network system could classify or predict the pest incidence as either 'high' or 'low' based upon economic threshold level with high degree of accuracy. Extracting rules from the decision tree helps the user to understand the role of biotic and abiotic factors on H. armigera incidence.
Keywords
Back-Propagation Algorithm, Biotic And Abiotic Factors, Helicoverpa armigera, Knowledge Extraction, Neuralnetwork Classifier, Pest Prediction.
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