Single-kernel mass determination for grain inspection using machine vision

被引:0
|
作者
Majumdar, S [1 ]
Jayas, DS [1 ]
机构
[1] Univ Manitoba, Dept Biosyst Engn, Winnipeg, MB R3T 5V6, Canada
关键词
machine vision; moisture content; growing regions; grain image-area; grain mass; wheat; barley; oats; rye; canola;
D O I
暂无
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
The effects of moisture content (m.c.) on artificially wetted grain mass were quantified for wheat (Canada Western Red Spring (CWRS) and durum), barley oats, and rye at moisture content between 10 and 22% (wet basis, w.b.) and for canola at moisture content between 5 to 15.5% (w.b.). The grain mass increased linearly with an increase in grain moisture content. To determine the effect of growing regions on the grain mass, experiments were conducted at two fixed moisture contents (13.5 +/- 0.5% and 16.5 +/- 0.5% w.b.) for CWRS wheat collected from 20 different regions in Western Canada. The variations in grain mass due to growing region were much larger than due to change in moisture content. Relationships between grain mass and other grain features extracted from images acquired for both dorsal (crease-down) and ventral (crease-up) positions were determined. Out of 13 features analyzed grain image-area showed the best correlation with grain mass (correlation coefficient = 0.91). The grain placement (dorsal or ventral view) did not significantly affect the above mentioned correlation.
引用
收藏
页码:357 / 362
页数:6
相关论文
共 50 条
  • [31] Cereal grain and dockage identification using machine vision
    Paliwal, J
    Visen, NS
    Jayas, DS
    White, NDG
    BIOSYSTEMS ENGINEERING, 2003, 85 (01) : 51 - 57
  • [32] Parametric Detection of Rice Kernel Shape Using Machine Vision
    Wang, Yaqin
    Gao, Hua
    Liang, Yong
    SENSOR LETTERS, 2011, 9 (03) : 1212 - 1219
  • [33] Inspection of Screw Holes on Machine Parts Using Robot Vision
    Baykal, Ibrahim Cem
    2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP), 2018,
  • [34] Colored rice quality inspection system using machine vision
    Chen, Shumian
    Xiong, Juntao
    Guo, Wentao
    Bu, Rongbin
    Zheng, Zhenhui
    Chen, Yunqi
    Yang, Zhengang
    Lin, Rui
    JOURNAL OF CEREAL SCIENCE, 2019, 88 : 87 - 95
  • [35] QUANTITATIVE AUTOMATED INSPECTION OF STANDARD PARTS USING MACHINE VISION
    ASOUDEGI, E
    COMPUTERS & INDUSTRIAL ENGINEERING, 1992, 23 (1-4) : 361 - 364
  • [36] A Study on the Elliptical Gear Inspection System Using Machine Vision
    Park, Jin Joo
    Kim, Gi Hwan
    Lee, Eung Seok
    TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A, 2014, 38 (01) : 59 - 63
  • [37] Automated post bonding inspection by using machine vision techniques
    Wang, MJJ
    Wu, WY
    Hsu, CC
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2002, 40 (12) : 2835 - 2848
  • [38] IN-PROCESS INTELLIGENT INSPECTION OF THE SPECIMEN USING MACHINE VISION
    Mahor, Adarsh
    Yadav, Ram Singar
    PROCEEDINGS OF ASME 2022 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2022, VOL 2B, 2022,
  • [39] RULE-BASED FABRIC INSPECTION USING MACHINE VISION
    李允明
    兰东
    Journal of China Textile University(English Edition), 1993, (03) : 28 - 41
  • [40] Steel surface in-line inspection using machine vision
    Liu, Hsiao-Wei
    Lan, Yu-Ying
    Lee, Han-Wen
    Liu, Ding-Kun
    FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION, 2016, 0011