Department of Industrial Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
Department of Production Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PLoS One. 2020 Nov 5;15(11):e0241888. doi: 10.1371/journal.pone.0241888. eCollection 2020.
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.
全球鸡蛋产品增加了 60%，导致农民或贸易商的鸡蛋产业。双黄蛋 (DY) 的价格比同大小的单黄蛋 (SY) 高出 35%左右。虽然将 DY 与 SY 分开会增加更多收入，但需要从熟练劳动力中以更高的成本进行分类。通常，分离 DY 鸡蛋需要通过鸡蛋的重量和形状由专业人员来进行，但仍然存在很高的误差。本研究的目的是使用模糊逻辑检测双黄蛋 (DY) 鸡蛋，并使用重量和鸡蛋大小的比例开发低成本原型以降低分离成本。首先使用 K-均值聚类来分离 DY 和 SY。然而，由于其硬聚类，该技术的误差仍然很高，达到 15.05%。因此，考虑使用重量和鸡蛋大小的比例输入 DY 和 SY 的交集散射，并使用模糊逻辑算法来提高误差。模糊逻辑的误差结果取决于输入隶属函数 (MF)。本研究选择重量的三角形 MF 为低 = 65 克，中 = 75 克，高 = 85 克，而鸡蛋的比例是三角形 MF 为低 = 1.30，中 = 1.40，高 = 1.50。该算法不提供最小总误差，而是提供低误差，以检测真实鸡蛋为 SY 的双黄蛋，其错误率为总鸡蛋的 1.43%。该算法应用于通过 Mbed 平台和 OpenMV CAM 开发双黄蛋检测原型，分别测量重量和鸡蛋的比例。
