Detection of Apple Leaf Disease Recognition Algorithm Based on KNN and Multi-feature Fusion
Accurate identification and prevention of apple leaf diseases can effectively improve the yield and quality of apples.As the research object of the common apple leaf diseases(rust,black rot and scab),A non-destructive detection model based on KNN and multi-feature fusion is constructed.The K-means clustering algorithm was used to segment the apple leaf image.The color,texture and shape features of the image were extracted by color moment,gray level co-occurrence matrix and Hu distance respectively.The characteristic parameters were trained of the classification model by the KNN algorithm,which can realize the purpose of green and accurate identification of apple leaf diseases.The experimental results showed that the accuracy of apple leaf disease recognition based on single feature detection of color,texture,and shape was 75％,57％,and 45％,respectively.The color feature is more intuitive with 9 features,and the recognition rate is higher.The shape feature is difficult to determine the K point when performing image segmentation,resulting in a low recognition rate.Based on color,texture,shape and other multi-feature fusion,13 features were extracted,It can accurately identify apple leaf diseases with a recognition rate of 84％,and provides technical support for the prevention and control of pests and diseases in green agricultural orchards.
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