Detection of Apple Leaf Disease Identification Based on GA-SVM and Feature Extraction
Accurate identification and prevention of apple leaf diseases can effectively improve the yield and quality of apples.To improve the accuracy of apple leaf disease identification,five common apple leaf diseases(spotted leaf disease,brown spot disease,mosaic disease,gray spot disease and rust disease)were regarded as research objects,the apple leaf image was segmented by threshold segmentation,and the shape features of the image were extracted by Hu moments.The genetic algorithm(GA)and support vector machine(SVM)were combined to classify and train the feature parameters,to identify the apple leaf diseases accurately.During the experiment,1 000 images from the sample library were used for the experiment,of which 700 samples were used for model training,and 300 samples were selected for model testing for five leaf diseases.The test results showed that the accuracy of the support vector machine prediction data optimized by the genetic algorithm was significantly improved.The recognition rates of apple leaf spot leaf,brown spot,mosaic disease,gray spot and rust disease were 94.5%,86.5%,95.5%,90.0%and 93.2%,respectively.
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