TITLE:
On Lemon Defect Recognition with Visual Feature Extraction and Transfers Learning
AUTHORS:
Yizhi He, Tiancheng Zhu, Mingxuan Wang, Hanqing Lu
KEYWORDS:
Machine Learning, Visual Feature Extraction, Convolutional Neural Networks, Transfer Learning
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.9 No.4,
September
2,
2021
ABSTRACT: 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 (i.e., 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 Geometry Group 16 based
(VGG16-based) network using transfer learning. The proposed model is compared
with many benchmark models such as K-nearest Neighbor
(KNN) and Support Vector Machine (SVM). Results 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.