Double yolk eggs detection using fuzzy logic.

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg's size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg's size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.