# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Pest and Disease Identification Based on RGB Image  
#### 2.1.1 Traditional Machine Learning Disease Recognition  
The traditional machine learning method first uses mobile devices such as mobile phones or cameras to capture RGB images of plant diseases and complete the collection of datasets. Then the preprocessing technology is used to remove the complex background of the image or segment unnecessary regions to highlight the disease area. Then, artificially designed image features are used to extract relevant information about the disease. Finally, supervised or unsupervised classifiers are used to classify the images. Among them, the corresponding feature extraction can be carried out by utilizing the characteristics of image phase, color space, texture, and shape. Figure 1 shows the traditional method identification process.  
[PERSON] [5] proposed a disease image segmentation method based on naive Bayes for citrus leaf canker disease. The method was compared with threshold method and a support-vector-machine-based segmentation method, and evaluated by combining subjective scatter plot analysis and image mis-segmentation rate. Although the naive-Bayes-based segmentation method takes a longer time, the mis-segmentation rate is the smallest. [PERSON] et al. [6] proposed an improved image retrieval algorithm based on HSV color histogram space for citrus disease monitoring, which uses color space to display color changes and perform non-uniform quantizing processing on leaf color. The proposed HSV non-uniform quantizing (16:4:4) can more clearly reflect the color characteristics of citrus diseases and reflect the global features of the image than the traditional scheme (8:3:3). [PERSON] [7] detected citrus canker disease collected outdoors, introduced a classification strategy, proposed a canker feature descriptor combined with leaf color and texture information, and used the AdaBoost algorithm to select the most important features. The experiment proves that this method has a high classification accuracy. [PERSON] et al. [8] used Delta E, RGB, HSV, and LBP as descriptors for citrus disease images. Compared with other classifiers, the bagged tree ensemble classifier has better discrimination ability for color features. The overall classification results show 99% accuracy, 1.0 area under the curve, and 99.7% sensitivity. [PERSON] [9] proposed a method for huanglongbing detection based on visible spectrum image processing and C-SVC. In addition to LBP features, feature vectors were constructed by using statistical information of hue, saturation, intensity, and gray image histogram, and dimension reduction was performed by PCA. [PERSON] [10] proposed a method for detecting diseased areas of citrus leaves. The RGB images were converted into YCbCr and lab space, and k-means clustering was used for segmentation to detect the diseased pixels on the leaves. Finally, the candidate regions were classified using grey level co-occurrence matrix features.  
Traditional machine learning methods are used to identify citrus diseases, such as studying the color space of disease leaves, changing disease segmentation methods, selecting prominent features, etc. Due to the complex and irregular diseased areas of crop disease images, it is difficult for traditional methods to select and extract the best features from the images for disease recognition, which requires a certain time, efficiency, and manpower for preprocessing. The process of manual extraction of disease features is slow, which has certain subjectivity and limitations, leading to low recognition accuracy.