Efficient Kernel-Based Fuzzy C-Means Clustering for Pest Detection and Classification

Plant pest detection is an essential for food security, quality of life and steady agricultural economy. Through rapid infestation by pests and insects various problems faced in enormous agricultural yield for every year. Many researches are being carried out worldwide to identify scientific methodologies for detection of pests. In the recent past, several approaches based on automation and image processing has come to light on the way to address this issue. In this paper we present a clustering technique, the popular algorithm for clustering is kernel-based fuzzy c-means clustering algorithm (KFCM) is used to identify density of pest in plant. A supervised learning neural network was applied to the classification of feature extraction of leaf. The methods studied are for increasing throughput & reducing subjective arising from human experts in detecting the pests in plant.