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Original Article

Precision Grape Plantation Management: Harnessing CS-ML Approaches

  • Paper ID 136
  • Volume 4, Issue 5
  • Pages 28-31
  • Published Online: September-October 2024
  • 9 Views
Open Access Peer Reviewed

Authors

  • Pandey Rajnish1
  • Kumar Chandan2
  • Singh Rajesh Kumar3

12 Department of Computer Science Engineering, Doon College of Engineering Technology, UP, India. 3Assistant Professor, Department of Computer Science Engineering, Doon College of Engineering Technology, UP, India.

Article Information

DOI
No DOI
Published Online
September-October 2024
Pages
28-31
Article Type
Original Article
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No DOI available for cross-reference

References

1. Arturo Aquino, Maria P. Diago, Borja Millan, Javier Tard Aguila, “A new methodology for estimating the grapevine berry number per
cluster using image analysis”, Biosystems Engineering, Vol. 156, pp. 80-95, 2017.
2. Tardaguila, J., Diago, M.P., Millán, B., Blasco, J., Cubero, S. García-Navarrete, O.L., and Aleixos, N., “Automatic estimation of the size
and weight of grapevine berries by image analysis”, CIGR- AgEng., 2012.
3. G. M. Dunn, S. R. Martin, "Yield prediction from digitial image analysis: a technique with potential for vineyard assessments prior to
harvest.", Australian Journal of Grape and Wine Research, 10, pp.196-198, 2004.
4. Chamelat, R., Rosso, E., Choksuriwong, A., Rosenberger, C., Laurent, H., and Bro, P., “Grape Detection by Image Processing”,
Proceedings of IEEE 32nd Annual Conference on Industrial Electronics (IECON 2006), Paris, France. pp. 3697–3702, 2006.
5. Rabatel, G., &Guizard, C., “Grape berry calibration by computer vision using elliptical model fitting”, 6th European Conference on
Precision Agriculture (ECPA 2007), Skiathos, Greece. pp. 581-587, 2007
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