The week's pick
Reseach Article
A Prediction Model for Population Dynamics of Cotton Pest (Thrips tabaci Linde) using Multilayer-Perceptron Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 67 - Number 4 |
| Year of Publication: 2013 |
| Authors: Jyothi Patil, V. D. Mytri |
10.5120/11384-6663
|
Jyothi Patil, V. D. Mytri . A Prediction Model for Population Dynamics of Cotton Pest (Thrips tabaci Linde) using Multilayer-Perceptron Neural Network. International Journal of Computer Applications. 67, 4 ( April 2013), 19-26. DOI=10.5120/11384-6663
Abstract
India's agricultural sector faces a series of problems when it comes to increasing crop productivity. Despite the efforts of researchers to discover productive agricultural practices, crop yield has not been the most pleasing, and one global reason stated for this poor crop yield is the insect pests. Predicting in advance the occurrence of peak activities of a given pest could enable the development of a suitable pest control mechanism that would initiate better production. Researchers have attempted to comprehend the pest population dynamics by applying analytical and other techniques on pest surveillance data sets. In this paper, An intelligent system for effectual prediction of pest population dynamics of Thrips Tabaci Linde (Thrips) on cotton (Gossypium Arboreum) crop is presented. The raw data used in the proposed system was obtained from the College of Agriculture, Raichur, India. Initially, the raw (pest surveillance) data is prepared by 1) Data preprocessing 2) Normalization and 3) Data transformation. The feed forward Multi-Layer Perceptron (MLP) Neural Network with backpropagation training algorithm is employed in the design of the intelligent system. The neural network is trained and tested with the data prepared. The experimental results portray the effectiveness of the proposed system in predicting pest population dynamics of Thrips on cotton crop. Moreover, a comparative analysis is performed between the proposed system and two of the existing works. The results showed that the proposed system based on feed forward neural networks was best suited for effective pest prediction.
References
- P. Krishna Reddy, "A framework of information technology-based agriculture information dissemination system to improve crop productivity", Current Science, vol. 88, no. 12, 25 June 2005.
- Raj Reddy, "Computing – The next 10 years", talk presented at Georgia Tech 10th anniversary convocation, April 6, 2001 (The presentation is available at http://www. rr. cs. edu/rrlong. html).
- Gio Wiederhold, "Information systems that also predict into future", Lecture Notes in Computer Science 2544, Springer_Verlag, pp 1-14, 2002.
- B. V. Ratnam, P. Krishna Reddy and G. S. Reddy, "eSagu: An IT based personalized agricultural extension system prototype – analysis of 51 Farmers' case studies", International Journal of Education and Development using ICT, vol. 2, No. 1, 2006.
- Vayyavuru Sreenivasulu and H. B. Nandwana, "Networking of Agricultural Information Systems and Services in India", INSPEL vol. 35, No. 4, pp. 226-235, 2001.
- Hong Zhang, Paul Georgescu, Lansun Chen, "An impulsive predator-prey model of integrated pest management", CEU, Dept. of Mathematics, 2007.
- Dangeti Ravi Shankar, Akhil Kodali, Pradeep Beerla, D. V. S Mohan Nimai, "Neural Networks in Cultivation", Fourth International Conference on Information Technology, pp. 255-260, 2007.
- Miguel A. Altieri, Clara I. Nicholls, "Soil fertility management and insect pests: harmonizing soil and plant health in agroecosystems", Soil & Tillage Research, pp. 203–211, 2003.
- Rajat Gupta, BVL Narayana, P. Krishna Reddy, G. V. Ranga Rao, "Understanding Helicoverpa armigera Pest Population Dynamics related to Chickpea Crop Using Neural Networks", Third IEEE International Conference on Data Mining, pp. 723- 726, 2003.
- Ch. Pavan Kumar V. Pavan Kumar, P. Krishna Reddy, G. Rama Murthy, "Yellow Stem Borer (pest) Attack Prediction System for Rice crop using Neural Networks and Bayesian Classification", International Institute of Information Technology, 2004.
- A. K. S. Huda, T. Hind-Lanoiselet, C. Derry, G. Murray and R. N. Spooner-Hart, "Examples of coping strategies with agro meteorological risks and uncertainties for Integrated Pest Management", ARCP, 2007.
- Muriel Gevrey and S. P. Worner, "Prediction of Global Distribution of Insect Pest Species in Relation to Climate by Using an Ecological Informatics Method", Journal of Economic Entomology vol:99, no:3, pp:979-986. 2006.
- Gang Liu , Hongyan Shen, Xuehong Yang and Yinbing Ge, "Research on Prediction about Fruit Tree Diseases and Insect Pests Based on Neural Network ", IFIP International Federation for Information Processing, Springer Boston, pp: 1861-2288, Volume 187,2005.
- Abedin, Sultanul, "Gossypium arboreum", in Ali, S. I. ; Qaiser, M. , Flora of Pakistan, 130, St. Louis: University of Karachi & Missouri Botanical Garden, pp. 30, 1979.
- Dr. S. Vennila, Dr. V. K. Biradar, Mr. M. Sabesh, Dr. O. M. Bambawale, "Know your Cotton Insect Pest THRIPS", Central Institute of Cotton Research, Nagpur, Crop protection folder series, 3 of 11, March 2007.
- P. Cunningham, J. Carney, and S. Jacob, "Stability problems with artificial neural networks and the ensemble solution", Artificial Intelligence in Medicine, vol. 20, no. 3, pp. 217-225, 2000.
- Y. Hayashi, R. Setiono, and K. Yoshida, "A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders", Artificial Intelligence in Medicine, vol. 20, no. 3, pp. 205-216, 2000.
- Martin T. Hagan, Howard B. Demuth, Mark H. Beale, "Neural Network Design", chapters 1-12, Thomson learning, 2001, ISBN: 0- 9717321-0-8. Savkovic-Stevanovic, "Neural networks for process analysis and optimization: modeling and applications", Computers & chemical engineering, Vol. 18, No. 11-12 (14 ref. ), pp. 1149-1155, 1994.
- Savkovic-Stevanovic, "Neural networks for process analysis and optimization: modeling and applications", Computers & chemical engineering, Vol. 18, No. 11-12 (14 ref. ), pp. 1149-1155, 1994.
- MR Narasingarao, R Manda, GR Sridhar, K Madhu, AA Rao, "A Clinical Decision Support System Using Multilayer Perceptron Neural Network to Assess Well Being in Diabetes", 36th Annual Scientific Meeting of the Research Society for the Study of Diabetes in India, 21, 22 and 23 November 2008.
- Venkatesan P, Anitha S. , "Application of a radial basis function neural network for diagnosis of diabetes mellitus", Current Science 2006; 91:1195-9.
- Raghu Korrapati, "A Bayesian Model Framework to determine patient compliance in Glaucoma cases", Publisher: Iuniverse Inc. ISBN: 9780595368396.
Index Terms
Keywords