Application of Multi-Feature Fusion Method in Fast Detection of Potato Images
In view of the problems of low recognition accuracy and low efficiency of the current potato image pest detection meth-ods.A fast detection method of potato diseases and insect pests is proposed,which combines the feature weighted fusion adaptive algorithm of principal component analysis and the improved support vector machine.The feature weighted fusion adaptive algo-rithm of principal component analysis completes feature block selection,principal component extraction,weighting and fusion.The fused feature adopts the idea of decision tree,and is classified step by step through the improved support vector machine.Com-parative analysis is carried out through experiments.The results show that the proposed method has higher detection accuracy and shorter execution time than traditional detection methods.The detection accuracy rates of pests,3 types of diseases and 10 types of pests are 98.45%,97.33% and 98.00%,respectively,and the running time is 4.81s,3.74s and 4.65s,respectively.The detection method provides a theoretical method and basis for the development of image pest and disease rapid detection technology.
Pest DetectionPotato ImagePrincipal Component AnalysisSupport Vector MachineRapid Detection
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