Multi Class Grading and Quality Assessment of Pomegranate Fruits Based on Physical and Visual Parameters

Abstract

Grading and quality assessment is an important aspect of post-harvest management in pomegranate fruits. In India, the quality assessment is usually performed manually by feeling the fruit in hand. This manual assessment poses a lot of disadvantages such as uncertainty, tediousness, time consumption etc. Moreover there are no well-organized grading systems for testing quality of pomegranates. Aim of the present research work is to eliminate such problems associated with manual quality assessment by incorporating Machine Intelligence and Digital Image Processing techniques. The present work precisely redefines the new quality parameters associated with the existing grading criteria. The research work also proposes a unique Effective Quality Assessment (EQA) algorithm comprising of a holistic approach towards the grading and quality assessment of pomegranate fruits. Results of the research work are found to be 97.83% by using Artificial Neural Networks.