Segmentation of Crop Nutrient Deficiency Using Intuitionistic Fuzzy C-Means Color Clustering Algorithm

Abstract

Nowadays, crop nutrient deficiency is common in most of the agricultural fields in India due to inadequate and imbalanced fertilization. The main aim of this work is to segment and calculate the percentage of nutrient deficiency which helps to predict the rate of fertilization needed for that crop. In this paper, a new intuitionistic fuzzy c-means color clustering algorithm (IFCM) is introduced using intuitionistic fuzzy sets (IFSs) with its distance function defined from similarity measure. Initially, all the experimental images are preprocessed. Then the preprocessed images are segmented by using the proposed clustering algorithm. The experimental results obtained by IFCM algorithm are compared with fuzzy c-means algorithm (FCM) to show the effectiveness of the proposed algorithm. Comparison results reveal that the proposed segmentation method is capable of segmenting uncertain crop images with nutrient deficiency.