Identification of Peanut Pods with Three or More Kernels by Machine Vision and Neural Network

被引:0
作者
Wang, Jason [2 ]
Yang, Wade W. [1 ]
Walker, Lloyd T. [2 ]
Rababah, Taha [3 ]
机构
[1] Univ Florida, Dept Food Sci & Human Nutr, Gainesville, FL 32611 USA
[2] Alabama A&M Univ, Dept Food & Anim Sci, Normal, AL 35762 USA
[3] Jordan Univ Sci & Technol, Dept Nutr & Food Technol, Irbid 22110, Jordan
关键词
peanut pod; kernel; machine vision; image processing; neural network; COMPUTER VISION;
D O I
10.1515/ijfe-2012-0009
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Separation of unshelled peanuts containing three or more kernels and then niche marketing them can potentially increase the value of unshelled peanuts and thus the profit of peanut producers or processors. Effective identification of peanut pods with three or more kernels is a critical step prior to separation. In this study, a machine vision system was teamed up with neural network technique to discriminate unshelled peanuts into two groups: one with three or more kernels and the other with two or less kernels. A set of physical features including the number of bumps, projected area, length and perimeter, etc., were extracted from the images taken and used to train an artificial neural network for discriminating the peanuts. It was found that among all the selected features, the length, the major axis length and perimeter have the best correlation with the number of kernels (correlation coefficient r = 0.87-0.88); the area and convex area have good correlation (r = 0.85); the eccentricity, number of bumps, and the compactness have relatively lower correction (r = 0.77-0.80); the solidity and the minor axis length have the least correlation to the number of kernels (r = -0.415-0.26). The best discrimination accuracy obtained for peanut pods with three or more kernels was 92.5% for the conditions used in this study.
引用
收藏
页码:97 / 102
页数:6
相关论文
共 10 条
[1]   Improving quality inspection of food products by computer vision - a review [J].
Brosnan, T ;
Sun, DW .
JOURNAL OF FOOD ENGINEERING, 2004, 61 (01) :3-16
[2]  
Dowell F. E., 1992, Food Control, V3, P105, DOI 10.1016/0956-7135(92)90041-8
[3]   Development of a novel approach to determine heating pattern using computer vision and chemical marker (M-2) yield [J].
Pandit, R. B. ;
Tang, J. ;
Liu, F. ;
Pitts, M. .
JOURNAL OF FOOD ENGINEERING, 2007, 78 (02) :522-528
[4]  
Pearson T, 1996, FOOD SCI TECHNOL-LEB, V29, P203, DOI 10.1006/fstl.1996.0030
[5]  
Poxton MG, 1986, P C AQ 86 AUT DAT PR
[6]   Predicting mechanical properties of fried chicken nuggets using image processing and neural network techniques [J].
Qiao, J. ;
Wang, N. ;
Ngadi, M. O. ;
Kazemi, S. .
JOURNAL OF FOOD ENGINEERING, 2007, 79 (03) :1065-1070
[7]   Rapid machine vision method for the detection of insects and other particulate bio-contaminants of bulk grain in transit [J].
Ridgway, C ;
Davies, ER ;
Chambers, J ;
Mason, DR ;
Bateman, M .
BIOSYSTEMS ENGINEERING, 2002, 83 (01) :21-30
[8]  
Schofield CP, 1996, AGENG 96 C MADR
[9]  
Wang Y, 2006, APPL ENG AGRIC, V22, P577
[10]   Discrimination of hard-to-pop popcorn kernels by machine vision and neural networks [J].
Yang, W ;
Winter, P ;
Sokhansanj, S ;
Wood, H ;
Crerer, B .
BIOSYSTEMS ENGINEERING, 2005, 91 (01) :1-8