Assessment of banana fruit maturity by image processing technique

Maturity stage of fresh banana fruit is an important factor that affects the fruit quality during ripening and marketability after ripening. The ability to identify maturity of fresh banana fruit will be a great support for farmers to optimize harvesting phase which helps to avoid harvesting either under-matured or over-matured banana. This study attempted to use image processing technique to detect the maturity stage of fresh banana fruit by its color and size value of their images precisely. A total of 120 images comprising 40 images from each stage such as under-mature, mature and over-mature were used for developing algorithm and accuracy prediction. The mean color intensity from histogram; area, perimeter, major axis length and minor axis length from the size values, were extracted from the calibration images. Analysis of variance between each maturity stage on these features indicated that the mean color intensity and area features were more significant in predicting the maturity of banana fruit. Hence, two classifier algorithms namely, mean color intensity algorithm and area algorithm were developed and their accuracy on maturity detection was assessed. The mean color intensity algorithm showed 99.1% accuracy in classifying the banana fruit maturity. The area algorithm classified the under-mature fruit at 85 % accuracy. Hence the maturity assessment technique proposed in this paper could be used commercially to develop a field based complete automatic detection system to take decision on the right time of harvest by the banana growers.