Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters

Authors

  • B. J. Dange Computer Engineering department, Sanjivani College of Engineering Kopargaon, (An Autonomous institute), Affiliated to Savitribai Phule Pune University Pune, Maharashtra, India
  • Punit Kumar Mishra Marketing Management, Symbiosis Centre for Management Studies, Symbiosis International (Deemed University) Pune, Maharashtra, India
  • Kalpana V. Metre MET's Institute of Engineering, Nashik, Maharashtra, India
  • Santosh Gore Director, Sai Info Solution, Nashik, Maharashtra, India
  • Sanjay Laxnamrao Kurkute Savitribai Phule Pune University, Pravara Rural Engineering College Loni, Maharashtra, India
  • H. E. Khodke Assistant Professor, Computer Engineering department, Sanjivani College of Engineering Kopargaon, (An Autonomous institute), Maharashtra,India,423603, Savitribai Phule Pune University, Pune, India
  • Sujata Gore Director, Sai Info Solution, Nashik, Maharashtra, India

Keywords:

Grape prediction, CNN, machine learning, image processing

Abstract

The agricultural industry is adopting advanced technologies and applications like yield prediction, precision agriculture, and automated harvesting to enhance production and quality. Machine learning (ML) and computer vision are increasingly used for fruit detection, segmentation, and counting. Specifically, the use of Convolutional Neural Networks (CNN) in grape yield prediction and quality assessment is gaining popularity due to its high accuracy and cost efficiency. Additionally, a new methodology based on image analysis has been developed for fast and inexpensive cluster yield component determination in the wine and table grape industry.    

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Published

12.07.2023

How to Cite

Dange, B. J. ., Mishra, P. K. ., Metre, K. V. ., Gore, S. ., Kurkute, S. L. ., Khodke, H. E. ., & Gore, S. . (2023). Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters . International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 239–244. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3113

Issue

Vol. 11 No. 9s (2023)

Section

Research Article

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