# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Identification of Pests and Diseases Based on Spectral Technology  
With the development of modern agriculture, spectral imaging technology has been gradually applied to the identification of crop diseases, instead of the traditional detection methods. By combining spectral analysis technology and image analysis technology, spectral imaging technology can conduct qualitative and quantitative analysis on the tested objects and realize the rapid detection of citrus diseases [24]. Compared with traditional recognition technology, hyperspectral imaging technology can achieve non-destructive, real-time, and highly accurate detection objects, acquire image information of targets in any wave band, acquire spectral information of each pixel on the image, and detect changes in internal physiological information of early crop infection diseases [25]. Figure 4 shows the test diagram of hyperspectral imaging system. Hyperspectral characteristics are unique, and there are obvious differences between healthy and infected spectra, so it is possible to identify citrus diseases early based on hyperspectral characteristics. [PERSON] et al. [26] used a hyperspectral imaging instrument to collect the reflectance of the regions of interest within 478-900 nm of citrus healthy leaves, canker leaves, herbicide-damaged leaves, red spider leaves, and sooty-mold-diseased leaves as spectral information, and preprocessed the spectral information by using three methods: first-order derivative, multiple scattering correction, and standard normal transformation. PCA was used to extract characteristic wavelength from the preprocessed data, and SVM and random forest models were established to identify the disease. In 2023, [PERSON] et al. [27] proposed a bidirectional gated recurrent unit recurrent neural network (Att-BiGRU-RNN) classification model that integrated an attention mechanism, which can extract deep features from spectral information and improve the contribution rate of important spectral features to the classification model. The overall classification accuracy can reach 98.21%. [PERSON] et al. [28] proposed a non-destructive detection method for huan-glongbing in the fields based on hyperspectral reflectance. The feature strap extraction method based on entropy distance and sequential backward selection provides a choice for dimensionality reduction, and uses machine learning algorithms such as logistic regression, decision tree, support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning to classify diseases. [PERSON] et al. [29] used image processing methods such as principal component analysis, pseudo color image conversion technology, and improved watershed segmentation algorithm (IWSA) to analyze the feasibility of detecting citrus decay based on scanned sound and hyperspectral transmission images (325-1098 nm) of decayed citrus. Figure 4: Test diagram of hyperspectral imaging system. Citrus diseases are complex and changeable, and some similar diseases are difficult to identify. In addition to the above processing of spectral information to improve the classification accuracy, some researchers use the supervised partial least square discriminant analysis method to better classify and identify different diseases. [PERSON] et al. [30] collected five kinds of hyperspectral images of citrus leaves in the 380-1080 nm spectral range, and established two discriminative model, namely, least squares support vector machine (LS-SVM) and partial least squares discriminant analysis (PLS-DA), to conduct nondestructive detection and disease grade discrimination for citrus huanglongbing. The results show that the latter has better prediction ability, with a misjudgment rate of only 5.6%. [PERSON] et al. [31] extracted the region of interest of the hyperspectral image on the surface of the collected citrus leaves, counted the average spectral data, carried out vegetation and plant operations, and identified and classified diseases through the PLS-DA discriminative model. [PERSON] et al.