Study of population dynamics of soybean semi-looper Gesonia gemma Swinhoe by using rule induction model in Maharashtra, India

DOI: 10.18805/lr.v0i0.7297    | Article Id: LR-3571 | Page : 369-373
Citation :- Study of population dynamics of soybean semi-looper Gesonia gemmaSwinhoe by using rule induction model in Maharashtra, India .Legume Research.2017.(40):369-373

J. Cruz Antony# and M. Pratheepa*

Address :

Division of Molecular Entomology, ICAR-National Bureau of Agricultural Insect Resources, Bengaluru-560 024, India.

Submitted Date : 23-07-2015
Accepted Date : 16-12-2015


Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 rule induction) has been proposed for rule induction model to generate a list of classification rules with target feature (G. gemma population) and the independent abiotic features. The classification rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this rule induction model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.


CN2 rule induction Gesonia gemma Population dynamics Semi-looper Soybean.


  1. Anderson Brigham and Moore. Andrew (1998). ADtrees for Fast Counting and for Fast Learning of Association Rules. Knowledge Discovery from Databases, 134-138.
  2. Chu Wesley and Young Lin Tsau (2005). Foundations and Advances in Data Mining. Springer Science & Business Media, Germany. 
  3. Clark, P and Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3: 261-283.
  4. Meena, K., Pratheepa, M., Subramaniam, K. R., Venugopalan, R and Bheemanna, H. (2010). A Decision tree induction approach to study the population dynamics of Helicoverpa armigera (Hübner) and its natural enemies on cotton. Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, 1: 580-    586. 
  5. Netam, H. K., Gupta, R and Soni, S. (2013). Seasonal incidence of insect pests and their biocontrol agents on soybean. IOSR-JAVS, 2: 07-11. 
  6. Ramaraj, M and Thanamani, A. S. (2013). A comparative study of CN2 rule and SVM algorithm and prediction of heart disease datasets using clustering algorithms. Network and Complex Systems, 3: 1-7.    Yadav, S. S., Nayak, M. K., Srivastava, A. K., Gupta, M. P and Tomar, D.S. (2014). Population dynamics of insect defoliator of soybean and correlation with weather parameters. Ann. Pl. Protec. Sc, 22: 208-209.

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