Determination of surface color of ‘all yellow’ mango cultivars using computer vision - Nagle, Marcus; Intani, Kiatkamjon; Romano, Giuseppe; Mahayothee, Busarakorn; Sardsud, Vicha; Müller, Joachim (International Journal of Agricultural and Biological Engineering, 2016)
Image processing techniques are increasingly applied in sorting applications of agricultural products. This work has assessed the use of image processing for inspecting surface color of two Thai mango cultivars. A computer vision system (CVS) was developed and experiments were conducted to monitor peel color change during the ripening process. Conversion of RGB to CIE-LAB values was done via image processing and prediction models were developed to estimate color parameters from CVS data. Performance evaluations showed insufficient prediction for L values (R2 = 0.42-0.58), but better results for A and B values (R2 = 0.90-0.95 and 0.80-0.82, respectively). Compared to the calculated color values hue angle and chroma, a yellowness index computed from intermediate XYZ values was found to be much more adept at accurately predicting peel color from CVS data. Correlations were strong for both cultivars (R2 = 0.93 for ‘Nam Dokmai’ and R2 = 0.95 for ‘Maha Chanok’). Results from classification analysis indicated satisfactory results for classifying fruits according to ripeness based on yellowness. Success rates of true positives in the categories unripe, ripe and overripe ranged 72%-92% for ‘Nam Dokmai’ and 98%-100% for ‘Maha Chanok’. Therefore, it was shown that the CVS was capable of producing accurate color values for the two mango cultivars investigated. The findings of this study can be incorporated for development of a robust system for quality prediction and establishment of a CVS for automatic grading and sorting of mangos.Keywords: mango, peel color, computer vision, image processing, fruit quality, ThailandDOI: 10.3965/j.ijabe.20160901.1861Citation: Nagle M, Intani K, Romano G, Mahayothee B, Sardsud V, Müller J. Determination of surface color of ‘all yellow’ mango cultivars using computer vision. Int J Agric & Biol Eng, 2016; 9(1): 42－50.