# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Multi-Feature Fusion  
The similarity between weeds and crops makes using a single image feature to detect weeds and crops almost impossible. The commonly used image features can achieve the purpose of weed detection, but the experimental accuracy is low and the stability is poor in a nonideal environment due to the complex interference factors in the actual field. Table 4 indicates that the four features are from different perspectives and complement one another in function. To improve the experimental accuracy, researchers have successively used the method of multi-feature fusion to solve the problem of weed detection. [PERSON] et al. (2019) combined statistical GLCM, structural method fast Fourier transform, and scale-invariant feature transform and achieved more than 80% accuracy in the real-time weed control system of an oil palm plantation. [PERSON] et al. (2019) used a method based on the combination of shape analysis and spectral angle matching to identify weeds in watermelon fields. Shape and spectral features were used separately, excluding texture features. [PERSON] et al. (2019) focused on vegetation classification on the basis of features extracted from a local binary model and GLCM and classified images in accordance with the density of grass to highlight the images with potential fire risks on both sides of the road. [PERSON] (2018) constructed a leaf texture feature extraction algorithm based on GGCM and an improved leaf color feature extraction algorithm combining K-means and SVM for plant leaf recognition. However, the problems of extracting leaf images and performing threshold segmentation under a complex background remain. [PERSON] et al. (2017) extracted three types of features of plant leaf shape, texture, and fractal dimension on the basis of field plant image processing. Compared with single-feature recognition, the multi-feature decision making fusion recognition method has better stability and higher accuracy, but it does not analyze the problem of feature selection. [PERSON] et al. (2019) studied the method of multi-feature fusion based on field weed detection at the corn seedling stage to analyze the selection of common feature descriptor combinations. On the basis of 6 feature descriptors commonly used in recent years (rotation-invariant LBP, HOG, GLCM, GGCM, Hu moment invariant, and Gabor), 18 multi-feature groups were formed. The combination of rotation-invariant LBP feature and GGCM showed the highest accuracy. Experiments have also proven that the average accuracy of multi-feature fusion is not necessarily higher than that of single-feature fusion. [PERSON] et al. (2010) used three single features, namely, shape, color, and texture, or fusion features of Malaysian herbal plant leaves for identification experiments. The experimental results showed that when the three features were fused, the average accuracy was highest, followed by the average accuracy when using only the texture features. When shape features are combined with texture features, the average accuracy decreased. [PERSON] et al. (2018) studied the feasibility of integrating spectral, shape, and texture features to identify corn and seven kinds of weeds. They found that from the perspective of accessibility of crop/weed discriminant features, spectral and shape features can be used as the optimal features to develop weed identification. Nonetheless, such a method has not been applied in a complex natural environment, and the method needs further research. [PERSON] et al. (2019) proposed a new shape feature, MTD, which was combined with the LBP-HF texture feature for leaf classification and retrieval tasks. This method is efficient and suitable for large-scale plant species identification. However, its features