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 Mature broad fingers on cam value of their images precisely.
A total of 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 Hence the maturity assessment Mature broad fingers on cam 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.
Banana is a globally consumed fruit and the fourth most important food crop along with rice, wheat and maize in the world. It provides livelihood and nutritional security to millions of people across the globe.
Banana is grown in countries across the world in an area of 4. This contradictory in production and export of banana fruit among these countries are mainly due to the perishable nature of banana and lack of knowledge and technical know-how of fruit quality standard to match the international standard Patil and Rawale Maturity of the banana bunches dictate the quality of fruit during ripening and overall marketability.
Major post harvest loss could be managed by harvesting the fruit at proper maturity stage Chegeh et al. Early harvest of banana may lack flavour and may not ripe properly, while harvesting it late may over ripe the fruit and cause splitting Patil and Rawale Banana is harvested at over mature stage for local market distribution than those which are meant for export.
Hence, it is Mature broad fingers on cam to harvest fruit at the right maturity stage to suit the purpose. Maturity of the fruit is assessed by measures such as change of peel color from dark green to pale green, disappearance of angularity, finger length and diameter. Based on these maturity indices, the banana bunches can be classified into three categories viz.
The hand-calibers and scales are also used to assess optimum finger diameter and length. The manual sorting of banana fruit bunches is a time consuming, labour-intensive process resulting in bias and human error, which drastically affects the growers profitability.
Hence there is an urgent need for a reliable, rapid and accurate automatic detection technique for assessing the banana fruit maturity.
Automatic detection system, based on camera with computer based technology has been widely explored for the quality analysis and grading of agricultural products in recent years.
This is known as computer vision system or computerised image analysis technique and it Mature broad fingers on cam proven to be successful for objective measurement of various fruit crops Bato et al. This system basically consists of standard illumination, a digital camera for image capturing and computer software for image processing Mendoza and Aguilera Image processing is an innovative field of science where the acquired image is transformed into useful information.
In recent years, image processing has become an incredible part in various disciplines like medical, communication, geographical information system and so on. However in agriculture, image processing is in its initial progress stage of practical application. The image processing has been now widely suggested to assess the quality of fruit from the images Quevedo et al.
This has been possible because the image texture reflects the changes in intensity value, which might contain information about the color and the physical structure of the objects. Subsequently this technology has been investigated in strawberry Bato et al.