期刊
JOURNAL OF FOOD PROCESS ENGINEERING
卷 40, 期 6, 页码 -出版社
WILEY
DOI: 10.1111/jfpe.12558
关键词
-
资金
- Mechanical Engineering of Biosystems Department, Ilam University, Ilam
Shape-based classification of fruits and vegetables is one of the most important applications of image processing and machine vision technology in post-harvest processing of agricultural products. In this research, desirable (cylindrical), and undesirable (curved and conical) shapes of cucumber fruit were considered to be intelligently detected using image processing technique and artificial neural networks method. A new algorithm was programed for preprocessing and extraction of shape features from the images in MATLAB 2010a software. Beside common features, two new features including centroid non homogeneity and width non homogeneity were introduced and extracted. After feature selection, different neural network models were evaluated to classify the useful features. The best classifier model had accuracy of 97.1% with 4-20-2 structure. Practical applicationsThe present research introduces new features to distinguish cucumber shape: desired (cylindrical) and undesired. The approach can be used to develop a sorting system based on machine vision to separate cucumber fruits to two classes in industry.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据