Investigating Advanced and Innovative Non-Destructive Techniques to Grade the Quality of Mangifera Indica.

Authors

  • CH. V. K. N. S. N. Moorthy Department of Mechanical Engineering, Vasavi College of Engineering, Hyderabad, India,
  • Mukesh kumar Tripathi Department of Computer Science & Engineering, Vardhaman College of Engineering, Hyderabad, India,
  • Nilesh Pandurang Bhosle Department of Information Technology, Trinity Academy of Engineering, Pune, India,
  • Vishnu Annarao Suryawanshi Department of Electronics and Telecommunication, Trinity Academy of Engineering, Pune, India
  • Priyanka Amol Kadam Department of Computer Engineering, Trinity Academy of Engineering, Pune, India
  • Suvarna Abhimunyu Bahir Department of Computer Engineering, Trinity Academy of Engineering, Pune, India

Keywords:

Non-destructive, Near-infrared, quality grading, machine learning

Abstract

This paper comprehensively investigates various internal and external features to grade the mango. We have utilized different machine learning techniques and models to predict the quality of the mango fruit. One significant countenance of this paper is also to explore the Distinctiveness, robustness, and effectiveness of the existing state-of-the-art feature extraction techniques. We also investigate various aspects of destructive and non-destructive methods for the collection of data analysis. Near-infrared (NIR) based evaluation of mango fruit's internal quality is also an open challenge nowadays. The images of the same fruit under different lighting directions in visible light are negatively correlated. In contrast, closely correlated fruit images of the same individual are produced by Near-infrared imaging. Moreover, Near-infrared imaging is more advantageous for indoor and cooperative users. This concludes that Near-infrared (NIR) is very effectively used in several applications as a feature.

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Published

25.12.2023

How to Cite

Moorthy, C. V. K. N. S. N. ., Tripathi, M. kumar ., Bhosle, N. P. ., Suryawanshi, V. A. ., Kadam, P. A. ., & Bahir, S. A. . (2023). Investigating Advanced and Innovative Non-Destructive Techniques to Grade the Quality of Mangifera Indica. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 299–309. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3903

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

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