20.7.3.7.1 Inspection of Food Grains

Chapter Contents (Back)
Food Inspection. Plant Inspection. E.g. Rice, wheat, cereals
See also Agriculture, Inspection -- Food Products, Plants, Farms.

Brogan, W.L.[William L.], Edison, A.R.[Allen R.],
Automatic classification of grains via pattern recognition techniques,
PR(6), No. 2, October 1974, pp. 97-103.
Elsevier DOI 0309
BibRef

Davies, E.R.,
Image Processing For The Food Industry,
World Scientific2000, ISBN: 981-02-4022-8 .
WWW Link. BibRef 0001

Davies, E.R., Bateman, M., Mason, D.R., Chambers, J., Ridgway, C.,
Design of efficient line segment detectors for cereal grain inspection,
PRL(24), No. 1-3, January 2003, pp. 413-428.
Elsevier DOI 0211
BibRef

Sun, C.M.[Chang-Ming], Berman, M.[Mark], Coward, D.[David], Osborne, B.[Brian],
Thickness measurement and crease detection of wheat grains using stereo vision,
PRL(28), No. 12, 1 September 2007, pp. 1501-1508.
Elsevier DOI 0707
Grain thickness measurement; Grain crease detection; Stereo vision BibRef

Pujari, J.D.[Jagadeesh D.], Yakkundimath, R.[Rajesh], Byadgi, A.S.[Abdulmunaf S.],
Detection and classification of fungal disease with Radon transform and support vector machine affected on cereals,
IJCVR(4), No. 4, 2014, pp. 261-280.
DOI Link 1411
BibRef

Singh, K.R.[Kshetrimayum Robert], Chaudhury, S.[Saurabh],
Comparative analysis of texture feature extraction techniques for rice grain classification,
IET-IPR(14), No. 11, September 2020, pp. 2532-2540.
DOI Link 2009
BibRef


Prayuktha, B.[Bendadi], Vishali, M.[Mankina], Alessandro, D.[Distante], Rodolfo, G.[Guzzi],
Quick Quality Analysis on Cereals, Pulses and Grains Using Artificial Intelligence,
LPLF22(372-383).
Springer DOI 2208
BibRef

Velesaca, H.O., Mira, R., Suárez, P.L., Larrea, C.X., Sappa, A.D.,
Deep Learning based Corn Kernel Classification,
AgriVision20(294-302)
IEEE DOI 2008
Kernel, Image segmentation, Impurities, Feature extraction, Task analysis, Image color analysis, Pipelines BibRef

Belan, P.A.[Peterson A.], de Macedo, R.A.G.[Robson A. G.], Pereira, M.M.A.[Marihá M. A.], Alves, W.A.L.[Wonder A. L.], de Araújo, S.A.[Sidnei A.],
A Fast and Robust Approach for Touching Grains Segmentation,
ICIAR18(482-489).
Springer DOI 1807
BibRef

Hansen, M.A.E.[Michael A.E.], Kannan, A.S.[Ananda S.], Lund, J.[Jacob], Thorn, P.[Peter], Sasic, S.[Srdjan], Carstensen, J.M.[Jens M.],
State Estimation of the Performance of Gravity Tables Using Multispectral Image Analysis,
SCIA17(II: 471-480).
Springer DOI 1706
Gravity tables are machines that separate dense grains from lighter ones. BibRef

Patil, N.K., Yadahalli, R.M.,
The Effect of Block Size, Training Set and K-Value in the Classification of Food Grains Using HSI Color Model,
NCVPRIPG11(50-53).
IEEE DOI 1205
BibRef

Chen, L.J.[Li-Jun], Ren, W.T.[Wen-Tao], Li, Y.K.[Yong-Kui],
Fast location of corn images based on position features,
IASP10(272-275).
IEEE DOI 1004
BibRef

Xun, Y.[Yi], Yang, Q.H.[Qing-Hua], Bao, G.[Guanjun], Gao, F.[Feng], Li, W.[Wei],
Recognition of Broken Corn Seeds Based on Contour Curvature,
CISP09(1-5).
IEEE DOI 0910
BibRef

Larsen, R.[Rasmus], Arngren, M.[Morten], Hansen, P.W.[Per Waaben], Nielsen, A.A.[Allan Aasbjerg],
Kernel Based Subspace Projection of Near Infrared Hyperspectral Images of Maize Kernels,
SCIA09(560-569).
Springer DOI 0906
BibRef

Wiwart, M.[Marian], Koczowska, I.[Irena], Borusiewicz, A.[Andrzej],
Estimation of Fusarium Head Blight of Triticale Using Digital Image Analysis of Grain,
CAIP01(563 ff.).
Springer DOI 0210
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Plant Phenotyping .


Last update:Apr 18, 2024 at 11:38:49