摘要
Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (</span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;">, green and mold defects). First, the data enhancement and brightness compensation techniques are used for data prepossessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geome</span><span style="font-family:Verdana;">try Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as</span><span style="font-family:Verdana;"> K-</span></span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;">earest</span><span style="font-family:""> </span><span style="font-family:Verdana;">Neighbor (KNN) and Support Vector Machine (SVM). Result</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.
Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (</span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;">, green and mold defects). First, the data enhancement and brightness compensation techniques are used for data prepossessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geome</span><span style="font-family:Verdana;">try Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as</span><span style="font-family:Verdana;"> K-</span></span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;">earest</span><span style="font-family:""> </span><span style="font-family:Verdana;">Neighbor (KNN) and Support Vector Machine (SVM). Result</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.