Neural Networks in Cultivation

Author

Shankar, Dangeti Ravi ; Kodal, Akhil ; Beerla, Pradeep ; Nimai, D. V S Mohan

fYear

2007

fDate

2-4 April 2007

Firstpage

255

Lastpage

260

Abstract

Indian farming community is facing multitude of problems. Insect pests are supposed to be a major constraint to the crop productivity. The main problem in addressing the issue of pest management is inadequate knowledge about the factors influencing the pest dynamics. In this paper an effort has been made to compare the efficiency of the prediction systems using the regression modeling, Bayesian analysis and neural network techniques. This was achieved by understanding the pest dynamics of Spodptera Litura on the groundnut crop with the data provided by ICRISAT. The results show that neural network system is the only one giving results with a very high degree of accuracy and is best suited to build a pest prediction system for groundnut crop

Keywords

belief networks; farming; neural nets; regression analysis; Bayesian analysis; Indian farming community; Spodptera Litura; crop productivity; cultivation; groundnut crop; neural networks; pest management; regression modeling; Bayesian methods; Biological system modeling; Crops; Data analysis; Databases; Insects; Knowledge management; Neural networks; Predictive models; Productivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, 2007. ITNG '07. Fourth International Conference on

Conference_Location

Las Vegas, NV

Print_ISBN

0-7695-2776-0

Type

conf

DOI

10.1109/ITNG.2007.133

Filename

4151693