Detection of cecal contaminants in visceral cavity of broiler carcasses using hyperspectral imaging

被引:0
|
作者
Park, B [1 ]
Lawrence, KC [1 ]
Windham, WR [1 ]
Smith, DP [1 ]
机构
[1] ARS, USDA, Richard B Russell Res Ctr, Athens, GA 30604 USA
关键词
machine vision; image processing; food safety; poultry inspection; hyperspectral; multispectral imaging; fecal contamination; ceca; internal contaminants;
D O I
暂无
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Detecting fecal contaminants in the visceral cavity of the broiler is difficult but extremely important for poultry safety inspection. A hyperspectral imaging system was used for detecting internal cecal contaminants on broiler carcass halves. Two 565- and 517-nm wavelength images were selected from 512 calibrated hypercube image data. Image processing algorithms including band ratio, threshold, and median filtering were used to identify fecal contaminants from the internal cavity. The accuracy of detection algorithms to identify cecal contaminants varied with fecal threshold values and median filter as well. The imaging system identified cecal contaminants with 92.5% detection accuracy but also incorrectly identified 123 carcass features that were not considered as contaminants (false positives) and missed 15 actual contaminants when a fecal threshold value of 1.05 was employed. The higher accuracy (96.9%) and lower missed contaminants were obtained when a different fecal threshold value was used. However, in this case, false positives markedly increased.
引用
收藏
页码:627 / 635
页数:9
相关论文
共 50 条
  • [21] Classification of contaminants from wheat using near-infrared hyperspectral imaging
    Ravikanth, Lankapalli
    Singh, Chandra B.
    Jayas, Digvir S.
    White, Noel D. G.
    BIOSYSTEMS ENGINEERING, 2015, 135 : 73 - 86
  • [22] Target Detection in Hyperspectral Imaging Using Logistic Regression
    Lo, Edisanter
    Ientilucci, Emmett
    ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XXII, 2016, 9840
  • [23] The detection of ductal carcinoma using noninvasive hyperspectral imaging
    Khouj, Yasser
    Dawson, Jeremy
    Coad, James
    Vona-Davis, Linda
    CANCER RESEARCH, 2017, 77
  • [24] Detection of kernels in maize forage using hyperspectral imaging
    Van Puyenbroeck, Emma
    Wouters, Niels
    Leblicq, Tom
    Saeys, Wouter
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 225
  • [25] Detection of Pits in Olive Using Hyperspectral Imaging Data
    Nasr-Esfahani, Shirin
    Muthukumar, Venkatesan
    Regentova, Emma E.
    Taghva, Kazem
    Trabia, Mohamed B.
    IEEE ACCESS, 2022, 10 : 58525 - 58536
  • [26] Anomaly detection using the hyperspectral polarimetric imaging testbed
    Cavanaugh, David B.
    Castle, Kenneth R.
    Davenport, Wayne
    ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XII PTS 1 AND 2, 2006, 6233
  • [27] Itchy Skin Region Detection using Hyperspectral Imaging
    Saleheen, Firdous
    Oleksyuk, Vira
    Won, Chang-Hee
    IMAGE SENSING TECHNOLOGIES: MATERIALS, DEVICES, SYSTEMS, AND APPLICATIONS V, 2018, 10656
  • [28] Melanoma Detection Using Smartphone and Multimode Hyperspectral Imaging
    MacKinnon, Nicholas
    Vasefi, Fartash
    Booth, Nicholas
    Farkas, Daniel L.
    IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES IX, 2016, 9711
  • [29] Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging
    Liu De-hua
    Zhang Shu-juan
    Wang Bin
    Yu Ke-qiang
    Zhao Yan-ru
    He Yong
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35 (11) : 3167 - 3171
  • [30] Citrus Black Spot Detection using Hyperspectral Imaging
    Kim, Dae G.
    Burks, Thomas F.
    Ritenour, Mark A.
    Qin, Jianwei
    PROCEEDINGS OF THE FLORIDA STATE HORTICULTURAL SOCIETY, VOL 126, 2013, 126 : 172 - 179