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Bheemanna, H.
- Seasonal Population Fluctuations of Cotton Bollworm, Helicoverpa armigera (Hubner) in Relation to Biotic and Abiotic Environmental Factors at Raichur, Karnataka, India
Abstract Views :213 |
PDF Views:122
Authors
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
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
Source
Journal of Biological Control, Vol 24, No 1 (2010), Pagination: 47-50Abstract
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.- Decision Tree Induction Model for the Population Dynamics of Mirid Bug, Creontiodes biseratense (Distant) (Hemiptera: Miridae) and Its Natural Enemies
Abstract Views :263 |
PDF Views:126
Authors
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
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
Source
Journal of Biological Control, Vol 27, No 2 (2013), Pagination: 88-94Abstract
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.References
- Anonymous, 2008a. Project coordinator’s report (2007– 08). All India co-ordinated cotton improvement project, pp. 4.
- Basak J, Krishnapuram R. 2005. Interpretable hierarchical clustering by constructing an un-supervised decision tree. IEEE Trans Knowl Data Engg 17 (1): 121–132.
- Ghavami S. 2008. The potential of predatory spiders as biological control agents of cotton pests in Tehran Provinces of Iran. Asian J Expl Sci. 22 (3): 303–306.
- Han J, Kamber M. 2001. Classification and prediction. pp. 285–375. In Data Mining Concepts and Techniques, 2nd ed. Jim Gray, Indian Reprint. Elsevier. Morgan Kaufmann.
- Khan M, Quade A, Murray D. 2007. Damage assessment and action threshold for mirids, Creontiades spp. In Bollgard II cotton in Australia. Second International Lygus Symposium Asilomar. J Insect Sci. 8: 49, p. 27.
- Patil BV, Bheemanna M, Patil SB, Udikery SS, Hosmani A. 2006. Record of mirid bug, Creontiades biseratense (Distant) on cotton from Karnataka, India. Insect Env. 11:176–177.
- Ravi PR, Patil BV. 2008. Biology of mirid bug, Creontiades biseratense (Distant) (Hemiptera: Miridae) on Bt cotton. Karnataka J Agric Sci. 21(2): 234–236.
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- Surulivelu T, Dhara Jothi B. 2007. Mirid bug, Creontiodes biseratense (Distant) damage on cotton in Coimbatore. http://www.cicr.gov.in
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- Venugopal Rao N. 1995. Bioecology and management of Helicoverpa armigera in the cotton ecosystem of Andhra Pradesh, Hyderabad, India. Andhra Pradesh Agricultural University. Ph.D Thesis.
- Zhao H, Ram S. 2004. Constrained cascade generalization of decision trees, IEEE Trans Knowl Data Engg 16 (6): 727–739.
- Neural-Network Classifier for the Prediction of Occurrence of Helicoverpa armigera (Hiibner) and its Natural Enemies
Abstract Views :221 |
PDF Views:127
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
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: 134-142Abstract
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.- Optimized Binning Technique in Decision Tree Model for Predicting The Helicoverpa armigera (Hubner) Incidence on Cotton
Abstract Views :249 |
PDF Views:114
Authors
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
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
Source
Journal of Biological Control, Vol 32, No 1 (2018), Pagination: 31-36Abstract
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.References
- Dhaliwal GS, Arora R. 1996. Integrated pest management: Achievements and Challenges, pp. 308–355. In: Dhaliwal GS, Arora R. (Eds). Principles of Insect Pest Management, NATIC, India.
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- Gupta GK. 2006. Classification. In: Introduction to Data Mining with Case Studies, Prentice-Hall of India, 106– 136. https://doi.org/10.1016/B978-044451636-7/50013-9
- Leonardo T, Miriam EP. 2002. The distribution and movement of cotton bollworm, Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) larvae on cotton. Philippine J Sci, 131: 91–98.
- Pratheepa M, Meena K, Subramaniam KR, Venugopalan R, Bheemanna H. 2011. A decision tree analysis for predicting the occurrence of the pest, Helicoverpa armigera and its natural enemies on cotton based on economic threshold level. Curr Sci. 100(2): 238–246.
- Shimazaki H, Shinomoto S. 2007. A method of selecting the binsize of a Time Histogram. Neural Comput.19(6): 1503–1527.
- SPSS V 17.0. 2008. Statistical Package for Social Sciences. SPSS Inc. Illinois, Chicago,USA.
- Sotiris K, Dimitris K. 2006. Discretization techniques: A recent survey. GESTS International Trans Comput. Sci Engineering. 32(1): 47–58.
- Zhao H, Ram S. 2004. Constrained cascade generalization of decision trees. IEEE Trans Knowledge Data Engineering. 16(6): 727–739. Available from: https://dl.acm.org/citation.cfm?id=1437601 https://doi.org/10.1109/TKDE.2004.3