A Design of Apple Leaf Disease Detection Algorithm Based on Wavelet Feature Extraction
For the green and non-destructive detection of common apple leaf diseases,an apple leaf disease recognition algorithm is proposed based on SVM and wavelet feature extraction.The algorithm performs wavelet transform on the apple leaf image.After extracting the wavelet coefficients,the wavelet packet transform is further performed to extract the representative wavelet basis features.According to the characteristic parameters of each region,a set of wavelet basis feature vectors is obtained.Then,the model is trained by SVM,and the SVM classifier is used to classify and identify different diseases.The experimental results show that the apple leaf disease recognition algorithm based on wavelet feature extraction has high accuracy and good reliability in identifying five common apple leaf diseases,which meets the needs of non-destructive detection of apple leaf diseases in actual production and provides technical support for green and intelligent fruit industry.
wavelet base characteristicsSVM trainingfeature extractionrecall rate
万方数据
维普
