INDEX

1. Understanding the Helicoverpa Armigera Pest Population Dynamics related to the chickpea crop using Neural NetworksRajat Gupta, B Narayana, Krishna Polepalli, G. Ranga Rao, C Gowda, Y. Reddy and G.Rama Murthy

2. INDEX • Introduction • Objective • Motivation • Pest Dynamics • Models Developed in the Past • Why they Failed ? • Preliminaries • Dataset Description • Results • Mean Graphs • Majority Voting • Conclusion

3. Introduction Helicoverpa Armigera Chickpea Crop

4. Participating Organizations International Institute of Information Technology (IIIT) International Crop Research for the Semi-Arid Tropics (ICRISAT)

5. Objective • To develop a pest forecasting mechanism by extracting pest dynamics from Pest surveillance database using Knowledge Discovery and Data Mining techniques. • To understand the interaction of various factors responsible for pest outbreaks.

6. Motivation • Insect pests are the major cause of crop loss. • The crop loss due to lack of advance information about pest emergence often leads to financial bankruptcy of the farmers.

7. Pest Dynamics • Highly dynamic nature of the Pest • Ability to adapt to new conditions quickly • Can migrate to long distances • Hibernate when condition are not favorable • Feeds on wide variety of hosts

8. Models Developed in the Past • Techniques used were essentially Statistical (Correlation and Regression Analysis) • T.P. Trivedi had proposed a regression model to predict the pest attack. • Model seems to work only for some years (1992-1994) • Correlation analysis was used by C.P. Srivastava to explore the relationship between the rainfall and pest abundance in different years. • The technique is not effective as the attributes don’t follow normal distribution

9. Why they FAILED? • Techniques used are able to capture only linear relationships. • Problems with the dataset (noisy data) • All events are treated equally

10. Pest Surveillance Dataset • Helicoverpa armigera pest data on Chickpea crop provided by International Institute for Semi-Arid Tropics (ICRISAT). • The dataset spans over a period of 11 years (1991-2001). • It contains information on 17 attributes.

11. Dataset Description • These Dataset contains 17 attributes which can be classified as • Weather parameters • Pest Incidence • Farm Parameters

12. Weather parameters • Rainfall • Relative Humidity • Minimum Temperature • Maximum Temperature • Sunshine hours.

13. Pest Incidence • Eggs/Plant • Larvae/Plant • Light Trap Catch • Pheromone Trap Catch

14. Farm Parameters • Zone • Location • Area Surveyed • Plant Protection • User • Season

15. Neural Networks • A Neural Network is an interconnected assembly of simple processing elements, units or nodes, called neurons. • The processing ability of the network is stored in the inter-unit connection strengths or weights. • Learns from a set of training patterns.

16. Multi Layer Neural Networks Inputs Outputs Hidden Layer

17. Why Neural Networks ? • Neural Networks don’t make any distributional assumption about the data. • It learns the patterns in the data, while statistical techniques try to do model fitting. This makes neural network modeling a powerful tool for exploring complex, nonlinear biological problems like pest incidence.

18. Data Preprocessing • Data Selection • Data Reduction • Null Values • Data Transformation • Normalization • Fourier Transform

19. Neural Network Training • Dataset • Advance Dataset (X) where X =0,12,3. • Training Dataset - 8 years (1991 - 1998) • Test Dataset - 3 years (1998 - 2001) • Learning Algorithm – Levenberg-Marquardt. • Bayesian Regularization • Hyperbolic Tangent Sigmoid function in hidden layers (2 hidden layers) • Linear Transfer function in outer layer

20. Datasets Generated • Advance (0) • Advance (1) • Advance (2) • Advance (3)

21. Average R-value

22. Larvae/Plant -Advance(0)

23. Larvae/Plant -Advance(1) Larvae/Plant -Advance(1)

24. Larvae/Plant -Advance(2) Larvae/Plant -Advance(2)

25. Larvae/Plant -Advance(3) Larvae/Plant -Advance(3)

26. Majority Voting(40%)

27. Majority Voting(50%)

28. Majority Voting(60%)

29. Conclusion • We can now predict the pest attack using Neural Networks two weeks in advance with high probability.

30. References • Data Mining Concepts and Techniques By Jiawei Han and Micheline Kamber • Neural Networks A Comprehensive Foundation By Simon Haykin • Applied Multivariate Statistical Analysis By By Richard Arnold Johnson, Dean A. Wichern, Dean W. Wichern. • Advanced Engineering Mathematics By Erwin Kreyzig. • Models for Pests and Disease Forecasting - T.P.Trivedi, D.K Das, A.Dhandapani and A.K. Kanojia • Das D.K , Trivedi T.P and Srivastava C.P 2001. Simple rules to predict attack of Helicoverpa armigera on crops growing in Andhra Pradesh, Indian Journal of Agricultural Sciences 71: 421-423. • Zhongua Zhao, Zuorui Shen .Theories and their applications of Stochastic Simulation Models for Insect population Dynamics. Department of Entomology, The China Agricultural University. ``http://www.cau.edu.cn/ipmist/chinese/lwzy/xuweilw/xwlw-zhzhao.htm`` • Agarwal, R., Imielinshki, T., Swami, A. 1993. "Mining association rules between sets of items in large databases" .Proc. of ACM-SIGMOD Int'l Conf. on Management of Data: 207-216. • Agarwal, R. Srikant, R., 1994, Fast Algorithms for Mining Association Rules, Proc. of the 20th VLDB: 487-499.