Ibrahim Mohd Firdaus, Ahmad Sa'ad Fathinul Syahir, Zakaria Ammar, Md Shakaff Ali Yeon
Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, Arau, Perlis 02600, Malaysia.
School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, Perlis 02600, Malaysia.
Sensors (Basel). 2016 Oct 27;16(11):1753. doi: 10.3390/s16111753.
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired -test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
传统的哈鲁玛尼斯芒果分级方法耗时、成本高且受人为偏差影响。在本研究中，开发了一种在线系统，利用计算机视觉对哈鲁玛尼斯芒果进行分类。该系统能够识别芒果形状的不规则性及其估计质量。一组不同大小和形状的芒果图像被用作数据库集。从每个图像中提取了一些重要特征，如长度、高度、质心和参数。傅里叶描述符和大小形状参数用于描述芒果形状，而圆盘法用于估计芒果的质量。通过逐步判别分析选择了四个特征，这在对规则和畸形芒果进行分类时很有效。使用配对检验和布兰德-奥特曼方法将排水法测得的体积与图像处理估计的体积进行了比较。两种测量结果之间没有显著差异（P>0.05）。对于由180个芒果组成的训练集，形状分类的平均正确分类率为98%。用另一个由140个芒果组成的测试集对数据进行验证，成功率为92%。同一组用于评估质量估计的性能。基于质量分级的分类平均成功率为94%。结果表明，使用机器视觉的在线分拣系统在根据水果形状和质量进行自动分拣方面具有很大潜力。
