Published in Scientific Papers. Series "Management, Economic Engineering in Agriculture and rural development", Vol. 23 ISSUE 3
Written by Mahmoud ELHOSARY, Adel ELMETWALLI, Asaad DERBALA, Salah ELSAYED

The aim of this research is to develop an image processing system that relies on machine vision to evaluate the chemical and physical properties in a non-destructive, fast and effective way to evaluate the quality of the orange fruits. Chemical and physical features such as TSS, titrated acidity, pH, TSS/T.acidity, liquid percentage, chlorophyll a, chlorophyll b, total chlorophyll and carotenoids were estimated. The results of the study showed that there is a relationship between the chemical and physical properties and the ripening of fruits. Relationships between R/G ratio range, G/R ratio, R/(R+G+B) range, NDVI1 index, VARI index, and VARI1 with some properties such as acidity, liquid percentage, pH, (TSS), TSS/ T.Acidity, chlorophyll a, chlorophyll b as well as the concentration of carotenoids at different ripening days. Correlation coefficient and multiple regression analysis were obtained by testing the correlation between (TSS), acidity, TSS/T. acidity, chlorophyll a, chlorophyll b and carotenoids, and ratios R/G ratio, R/(R+G+B), NDVI index and VARI index and VARI1 for orange fruits. The results showed that the mean of the indices of VARI, VARI1, NDVI1, and the R/G range provided a better indicator of the concentrations (TSS), acidity, TSS/T. acidity, and chlorophyll a and b for orange fruits. R/(R + G + B) ratio gave the highest regression coefficient with carotenoids (𝑅₂=95∗∗∗). while NDVI1 index gave the highest regression coefficient with chlorophyll a and chlorophyll b (𝑅₂=91∗∗∗and 𝑅₂=92∗∗∗), respectively.

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