Informatics Publishing Limited

M. Pratheepa ¹, K. Meena ², K. R. Subramaniam ³, R. Venugopalan ⁴, H. Bheemanna ⁵

Affiliations
1 National Bureau of Agriculturally Important Insects, Post Bag No. 2491, H. A. Farm Post, Hebbal, Bellary Road, Bangalore 560024, Karnataka, IN
2 Shrimathi Indira Gandhi College, Tiruchirappalli 620002, Tamil Nadu, IN
3 Department of M.C.A., Shrimathi Indira Gandhi College, Tiruchirappalli 620002, Tamil Nadu, IN
4 Indian Institute of Horticultural Research, Hessaraghatta Lake Post, Bangalore 560089, Karnataka, IN
5 Department of Entomology, Agricultural Research Station, Raichur 584101, Karnataka, IN

Abstract

An attempt was made to study the effect of abiotic and naturally occurring biotic factors on Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) with cotton as a model crop system. The results revealed that Chrysoperla sp. (carnea-group) (r = 0.344) was positively correlated with pest incidence and the weather parameters like maximum temperature (r = -0.309) and rainfall (r = -0.288) were negatively correlated with pest incidence. It was observed that post-monsoon season was most favourable for pest occurrence and it was more when the crop was in flowering and boll formation stage. Spiders and Chrysoperla sp. (carnea-group) were positively correlated with pest incidence during winter.

Keywords

Helicoverpa armigera, Cotton, Season, Spiders, Chrysopids.

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M. Pratheepa ¹, K. Meena ², K. R. Subramaniam ², H. Bheemanna ³

Affiliations
1 Department of M.C.A., Shrimathi Indira Gandhi College, Trichirappalli 620 002, Tamil Nadu, IN
2 Bharathidasan University, Trichirappalli 620 024, Tamil Nadu, IN
3 Department of Entomology, University of Agricultural Sciences, Raichur 584 102, IN

Abstract

The mirid bug, Creontiodes biseratense (Distant) (Hemiptera: Miridae) is as a serious pest of cotton crop. Forecasting model by linking the pest incidence with season, crop phenology, biotic and abiotic factors enable to understand the dynamics of pest occurrence likely to occur. A data mining technique decision tree induction model is proposed for forecasting the pest incidence and study the population dynamics of mirid bug, C. biseratense in relation to its natural enemies viz., spider Lycosa sp. and coccinellid Cheilomenes sexmaculata Fabricius and abiotic factors. The results of the decision tree agreed well with statistical analysis.

Keywords

Creontiodes biseratense, Cotton, Spiders, Coccinellids, Decision Tree, Information Theory, Abiotic.

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M. Pratheepa ¹, K. Meena ², K . R. Subramaniam ¹, R. Venugopalan ³, H. Bheemanna ⁴

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

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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M. Pratheepa ¹, J. Cruz Antony ², Chandish R. Ballal ¹, H. Bheemanna ³

Affiliations
1 ICAR-National Bureau of Agricultural Insect Resources, Bengaluru – 560024, Karnataka, IN
2 Department of Computer Science, Jain University, Bengaluru – 560011, Karnataka, IN
3 University of Agricultural Sciences, Agricultural Research Station, Raichur - 584102, Karnataka, IN

Abstract

The data mining technique decision tree induction model is a popular method used for prediction and classification problems. The most suitable model in pest forewarning systems is decision tree analysis since pest surveillance data contains biotic, abiotic and environmental variables and IF-THEN rules can be easily framed. The abiotic factors like maximum and minimum temperature, rainfall, relative humidity, etc. are continuous numerical data and are important in climate-change studies. The decision tree model is implemented after pre-processing the data which are suitable for analysis. Data discretization is a pre-processing technique which is used to transform the continuous numerical data into categorical data resulting in interval as nominal values. The most commonly used binning methods are equal-width partitioning and equal-depth partitioning. The total number of bins created for the variable is important because either large number of bins or small number of bins affects the accuracy in results of IF-THEN rules. Hence, optimized binning technique based on Mean Integrated Squared Error (MISE) method is proposed for forming accurate IF-THEN rules in predicting the pest Helicoverpa armigera incidence on cotton crop based on decision tree analysis.

Keywords

Bin Optimization, Decision Tree, Discretization, Helicoverpa armigera, If-Then Rules, Pest Prediction.

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