# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Classifier  
SVMs and Artificial Neural Networks (ANNs) have been widely used in crop and weed classification [102; 103]. SVMs can solve the problems of nonlinear and high-dimensional pattern recognition and have good performance in dealing with small-sample problems and nonlocal minimum problems. ANNs have a strong learning capability and can classify untrained data [63]. Other algorithms often involved in the literature include K-nearest neighbor (KNN) [104] and random forest [105; 106], naive Bayesian algorithm [107; 108], Bayesian classifier [109], and AdaBoost [110; 111].  
In recent years, relevant scholars have continued to study the use of various classifiers to identify and classify weeds. For instance, [PERSON] et al. [112] used a weed detection and image-processing algorithm based on ANN to distinguish weeds and crops in the soil background under uncontrolled outdoor light. [PERSON] et al. [113] used an improved KNN weed image classification method combined with GW and regional covariance Lie group structure to classify four kinds of broad-leaved weed images. The overall recognition accuracy was 93.13%. [PERSON] et al. [84] used SVM to identify 6 weeds in a dataset of 224 images, and the optimal combination of its extractor could achieve 97.3% accuracy. [PERSON] et al. [114] proposed a sequential classification method and used three different SVM models to distinguish not only weeds and barley but also weeds of monocotyled and dicotyledon plants.  
Some literature has utilized multiple classifiers. For example, [PERSON] and [PERSON] [51] evaluated the performance of using SVM and ANN based on shape features in accordance with the detection problem of four common weeds in sugar beet fields. The results showed that the overall accuracy of SVM was 95.00%, higher than that of ANN (i.e., 92.92%). [PERSON] et al. [115] proposed a method based on image segmentation and reconstruction to solve the problems of low recognition accuracy and invalid shape feature in the recognition process of overlapping leaves. The recognition results in different classifiers, such as SVM, KNN, DT, and naive Bayes, were compared using 78-dimensional features, such as color features, LBP texture features, and fractal box dimensions. The best was SVM. [PERSON] et al. [116] developed two kinds of rice field image classification technologies based on the density of weeds. The first method was to use GLCM combined with SVM to achieve a precision of 73%, and the second method was to use invariant scale and rotation moment based on a random forest classifier to achieve a precision of 86%. The limitation of the two methods is that they do not target other types of weeds, such as broadleaf weeds and sedges. [PERSON] et al. [117] implemented a machine vision-based method that can identify 10 types of weeds, including corn plants and specific species. This method uses a Gaussian classifier, a self-organizing feature map (SOFM), an SVM, and an autoencoder as the four hybrid classifiers. However, this method can only recognize four weeds with a maximum accuracy of over 90%. When applied in the field, the system error is relatively large.  
In summary, scholars have focused on improving classifiers based on machine vision or the corresponding image features of plants, which is of great significance to improve the accuracy. They can utilize the sample features in the case of small samples and do not require high hardware. They are conducive to practical deployment and play an important role in weed identification or classification in common scenes.