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| Adaptive center-symmetric local binary patterns for crop disease recognition |
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| DOI：10.16968/j.issn.1004-894X.2016.09.021 |
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| Abstract: |
| The local binary pattern（ LBP） based pattern recognition method has high histogram dimension，poor
discriminate ability and redundant information. According to the features of crop disease image，to accurately describe
the texture structure of crop disease leaf image，an adaptive center-symmetric local binary patterns（ ACSLBP）
algorithm was proposed for crop disease recognition. The invariance texture features of illumination and rotation
were extracted by the algorithm. Firstly，every disease leaf image was segmented using the fussy means-clustering
algorithm. Secondly，the segmented spot image was divided into several sub-images and their corresponding ACSLBP
texture histogram features were extracted respectively based on the adaptive threshold. Finally，combining the 6 color
features of spot image，the crop diseases were recognized by nearest neighbor classifier. The experiments on leaf
image database of four common cucumber diseases were implemented. The average correct recognition rate was above
95%. The experimental results verified that the proposed method was effective and feasible. |
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