Investigating Advanced and Innovative Non-Destructive Techniques to Grade the Quality of Mangifera Indica. - Moorthy, CH. V. K. N. S. N.; Tripathi, Mukesh kumar; Bhosle, Nilesh Pandurang; Suryawanshi, Vishnu Annarao; Kadam, Priyanka Amol; Bahir, Suvarna Abhimunyu (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.