Abstract
Arecanuts are among the main commercial crops of southern India. Identifying ripeness is important for harvesting arecanut bunches and directly affects the farmer’s profits. Manual identification and harvesting processes, however, are very tedious, requiring many workers for each task. Therefore, in recent years, image processing and computer vision-based techniques have been increasingly applied for fruit ripeness identification, which is important in optimizing business profits and ensuring readiness for harvesting. Thus, segmentation of arecanut bunches is required in order to determine ripeness. There are several techniques for segmenting fruits or vegetables after harvesting to identify ripeness, but there is no technique available for segmenting bunches before harvesting. In this chapter, we describe a computer vision-based approach for segmentation using active contouring, with the aim of identifying the ripeness of arecanut bunches. The experimental results confirm the effectiveness of the proposed method for future analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rathod, Kirankumar, M. Shivprasad, and Rajshekhar. 2015. Characterization and extraction of tannin from areca nut waste and using it as rust deactivator. International Journal of science, Engineering and Technology 366–372.
Mustafa, Nur Badariah Ahmad, Syed Khaleel Ahmed, Zaipatimah Ali, Wong Bing Yit, Aidil Azwin Zainul Abidin, and Zainul Abidin Md Sharrif. 2009. Agricultural produce sorting and grading using support vector machines and fuzzy logic. In IEEE international conference on signal and image processing applications.
Sarkate, Rajesh S., N.V. Kalyankar Dr, and P.B. Khanale Dr. 2013. Application of computer vision and color image segmentation for yield prediction precision. In International conference on information systems and computer networks.
Mery, Domingo, and Franco Pedreschi. 2005. Segmentation of color food images using a robust algorithm. Journal of Food Engineering 66: 353–360.
Rashidi, Majid and Keyvan Seyfi. 2007. Classification of fruit shape in kiwifruit applying the analysis of outer dimensions. International Journal of Agriculture and Biology; 15608530, 095759762.
Lee, Dah-Jye, James K. Archibald, and Guangming Xiong. 2011. Rapid color grading for fruit quality evaluation using direct color mapping. IEEE Transactions on Automation Science and Engineering 8 (2).
Pham, Van Huy, and Byung Ryong Lee. 2015. An image segmentation approach for fruit defect detection using K-means clustering and graph-based algorithm. Vietnam Journal of Computer Science 2 (1): 25–33. https://doi.org/10.1007/s40595-014-0028-3.
Danti, Ajit, and Suresha. 2012. Segmentation and classification of raw arecanuts based on three sigma control limits. Elsevier, Procedia Technology 4: 215–219.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Dhanesha, R., Shrinivasa Naika, C.L. (2019). A Novel Approach for Segmentation of Arecanut Bunches Using Active Contouring. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_69
Download citation
DOI: https://doi.org/10.1007/978-981-10-8797-4_69
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8796-7
Online ISBN: 978-981-10-8797-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)