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Classification of crops using FCM segmentation and texture, color feature
Anandkumar Patil, Lalitha Y S
Department of CSE, Appa Institute of Engineering and Technology, Gulbarga, Karnataka, India Department of ECE, Godutai College of Engineering for women, Gulbarga. Karnataka, India
Abstract: The objective of this study is to develop a FCM Segmentation that could distinguish crops as plant, soil and residue parts. This classification will help agronomist to decide crop pattern and cultivation practises. In this paper, collected the 10 different types of crops JPEG images from the fields. And stored as a database. After segmentation, color and texture features are applies to get the features of color and texture of the each crops. And the Euclidian distance algorithm used to identify the crops. Results show that classification accuracy is significantly improved. Hence, finally project has been demonstrated by using the plotted graphs. One for the accuracy of the images and another error rate in the classification of images.
Keywords: Image classification, FCM, Euclidian distance, Feature extraction etc.
Keywords: Image classification, FCM, Euclidian distance, Feature extraction etc.
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This work is licensed under a Creative Commons Attribution 4.0 International License.How to Cite:
[1] Anandkumar Patil, Lalitha Y S, “Classification of crops using FCM segmentation and texture, color feature,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE)