An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network

被引:11
作者
Kaya, Yilmaz [1 ]
Erez, Mehmet Emre [2 ]
Karabacak, Osman [2 ]
Kayci, Lokman [2 ]
Fidan, Mehmet [2 ]
机构
[1] Siirt Univ, Fac Engn & Architecture, TR-56100 Siirt, Turkey
[2] Siirt Univ, Fac Sci & Art, Dept Biol, TR-56100 Siirt, Turkey
关键词
Honey; pollen identification; expert system; GLCM; artificial neural network;
D O I
10.1080/00173134.2012.754050
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Pollen grains vary in colour and shape and can be detected in honey used as a way of identifying nectar sources. Accurate differentiation between pollen grains record is hampered by the combination of poor taxonomic resolution in pollen identification and the high species diversity of many families. Pollen identification determines the origin and the quality of the honey product, but this indefiniteness is also a big challenge for the beekeepers. This study aimed to develop effective, accurate, rapid and non-destructive analysis methods for pollen classification in honey. Ten different pollen grains of plant species were used for the estimation. GLCM (grey level co-occurrence matrix) texture features and ANN (artificial neural network) were used for the identification of pollen grains in honey by the reference of plant species pollen. GLCM has been calculated in four different angles and offsets for the pollen of the plant and the honey samples. Each angle and offset pair includes five features. At the final step, features were classified using the ANN method; the success of estimation with ANN was 88.00%. These findings suggest that the texture parameters can be useful in identification of the pollen types in honey products.
引用
收藏
页码:71 / 77
页数:7
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