Disease and Deficiency detection in Paddy Plant Using Soft Computing Technique

Authors

  • S. Sivagami, Dr. S. Mohanapriya

Abstract

Image processing widely used in agriculture sector for finding problems like disease identification, weed
detection, fruit grading etc., In this paper image processing used to identify the disease and deficiency of
paddy plant is proposed. In image processing first image is captured, then pre-processed after that
segmented and finally classified. Among these processes Image segmentation plays a vital role, it split the
given whole image into parts, among the parts we can choose which one is most important. There are wide
range of image segmentation algorithms available among them K-means is very simple to understand,
easy to implement and Produce accurate results. Though it is simple and accurate it has some limitations
that is we need to guess the value for K and randomly select K initial centroids among the given data
points. To overcome this initialization problem a new method for image segmentation based on Harmonic
search optimization (HSO) and K-means for deficiency detection was proposed. The performance of Kmeans algorithm is mainly based on the k value and k initial centroids of the clusters. Random initialization
is followed in normal K-means algorithm due to this normal k-means take lots of time to produce correct.
A new method called HSO based K-means is proposed to speed up the initialization process. The proposed
algorithms exploit an initial step derived from the HSO, considering Otsu method as the objective function.
After finding the cluster centers using HSO, K-means algorithm is initialised with these cluster centers.
Finally, segmentation result is compared with normal K-Means and Fuzzy C-Means segmentation
algorithm our proposed HSO based K-means algorithm gives better result than others.

Published

2020-11-01

Issue

Section

Articles