A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut

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Author(s): Siddesha S (Sri Jayachamarajendra College of Engineering, India), S K. Niranjan (Sri Jayachamarajendra College of Engineering, India)and V N. Manjunath Aradhya (Sri Jayachamarajendra College of Engineering, India)
Copyright: 2016
Pages: 28
Source title: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Pinaki Banerjee (Goldstone Infratech Limited, India), Dipankar Majumdar (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-9474-3.ch001

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Arecanut is an important cash crop of India and ranks first in the production. Arecanut crop bunch segmentation plays very vital role in the process of harvesting. Work on arecanut crop bunch segmentation is of first kind in the literature and this chapter mainly focuses on exploring different color segmentation techniques such as Thresholding, K-means clustering, Fuzzy C Means (FCM), Fast Fuzzy C Means clustering (FFCM), Watershed and Maximum Similarity based Region Merging (MSRM). The effectiveness of the segmentation methods are evaluated on our own collection of Arecanut image dataset of size 200.

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A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut

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