The aim of this study is to design, implement and evaluate an image-based software solution for automatic detection of abnormal on paddy fields that damages by insects, diseases. The methodology of the proposed is composed of third phases. This content is a combined approach for the segmentation of images. In the first phase, watershed segmentation algorithm, second phases, using the K-means clustering algorithm and final in the third phases reducing noise in image gradients. We have taken paddy filed images as a case study and evaluated the proposed approach using abnormal area paddy field images for the pest prediction. In experimental results, indicate that the proposed approach can automatic detection and of abnormal area on paddy fields for the pest prediction. In conclusion, the proposed methods show that detect abnormal region efficiently.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org