# Application of Machine Vision Technology in Citrus Production
## 3 Fruit Grading
### Maturity Grading  
The identification of citrus ripeness is an aspect of detecting the quality of citrus and a prerequisite for selective harvesting. Citrus with different ripeness has different uses. [PERSON] et al. [34] converted the acquired citrus RGB image into the Lab color space, finally selected a component significantly different from the background color to eliminate the background, and applied Hough circle transform to recognize and count mature citrus. [PERSON] et al. [35] used the color features of RGB images to generate feature vectors and conduct feature dimension reduction to obtain the best ROI for segmentation, and used SVM algorithm for segmentation and recognition. In the RGB color space, [PERSON] et al. [36] used the ratio transformation between R and G to obtain images that were orthogonal and weakly correlated with the fusion images of H and S. Combined with the mask method, object features were effectively extracted, and citrus fruits with different \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Dataset** & **Feature Extraction** & **Merits** & **Demerits** \\ \hline Traditional method & RGB image & \begin{tabular}{c} Manually extract \\ features \\ \end{tabular} & \begin{tabular}{c} Maturational theory, fast \\ calculation speed \\ Adaptable, \\ good learning capacity, good \\ portability \\ High resolution, \\ nondestructive examination, \\ wide range of information \\ \end{tabular} & Poor performance on complex problems \\ Deeping learning & RGB image & \begin{tabular}{c} Network automatic \\ extraction \\ \end{tabular} & \begin{tabular}{c} [PERSON] et al. \\ \end{tabular} & \begin{tabular}{c} [PERSON] et al. \\ \end{tabular} \\ Hyperspectral imaging & Hyperspectral image & \begin{tabular}{c} Feature band \\ image \\ extraction \\ \end{tabular} & \
\begin{tabular}{c} High resolution, \\ nondestructive examination, \\ wide range of information \\ \end{tabular} &
\begin{tabular}{c} High instrument cost, low \\ sensitivity, large \\ sample quantity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of traditional method, deep learning method, and hyperspectral imaging technique. maturity levels were effectively recognized. [PERSON], [PERSON] et al. [37] proposed a citrus fruit maturity method combining visual salience and convolutional neural network to identify the three maturity levels of citrus fruits. The improved visual saliency algorithm MSSS was used to detect that the brightness of mature fruits was high and uniform, followed by semi-mature fruits, and immature fruits that were very dark. After network comparison, the four-channel ResNet34 network was selected to combine the information of RGB images with the significance map to determine the fruit maturity level. [PERSON], [PERSON] et al. [38] proposed a method to distinguish orange ripening, unripening and scaling, or rotting, extracted image features based on boundary/internal pixel classification (BIC), including RGB color space and gray value, and studied the performance and application degree of three algorithms, naive Bayes, artificial neural network, and decision tree. The results show that decision tree is more effective than other techniques. [PERSON], [PERSON] et al. [39] developed a more robust deep convolutional neural network model to detect citrus maturity levels and black spot disease using an effective noise based data augmentation method that includes Gaussian, speckle, poison, and salt and pepper noise. [PERSON] et al. [40] used an industrial camera combined with a flipping mechanism to obtain the complete surface information of citrus in motion. After preprocessing, two-dimensional coloring ratio was obtained and its arithmetic average was taken to calculate the coloring rate for discrimination and classification. [PERSON] [41] converted the RGB color space of citrus into HSV color space and obtained H component. The color consistency was detected by tonal interval statistical method.