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

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

Siddesha S, S K. Niranjan, V N. Manjunath Aradhya
Copyright: © 2020 |Pages: 28
ISBN13: 9781522596219|ISBN10: 1522596216|EISBN13: 9781522596226
DOI: 10.4018/978-1-5225-9621-9.ch048
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MLA

S, Siddesha, et al. "A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut." Environmental and Agricultural Informatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1078-1105. https://doi.org/10.4018/978-1-5225-9621-9.ch048

APA

S, S., Niranjan, S. K., & Manjunath Aradhya, V. N. (2020). A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut. In I. Management Association (Ed.), Environmental and Agricultural Informatics: Concepts, Methodologies, Tools, and Applications (pp. 1078-1105). IGI Global. https://doi.org/10.4018/978-1-5225-9621-9.ch048

Chicago

S, Siddesha, S K. Niranjan, and V N. Manjunath Aradhya. "A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut." In Environmental and Agricultural Informatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1078-1105. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9621-9.ch048

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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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