# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods
### Traditional Features and Their Advantages and Disadvantages for Common Weed Detection
#### 3.1.1 Texture Features  
Texture features are regional features that reflect the spatial distribution among pixels, which have been widely used in image classification [56; 57; 58]. Plant leaves are usually flat, and different leaves have diverse vein texture and leaf surface roughness information. The texture information can be used to distinguish crops and weeds effectively. Texture feature methods can mainly be divided into four categories: (1) statistical method, (2) structural method, (3) model-based method, and (4) transform-based method [59]. The most common texture feature descriptors used in weed detection include GLCM [60] and Gray-level Gradient Co-occurrence Matrix (GGCM) based on statistical texture analysis methods, LBP based on structural texture analysis methods, fractal dimension based on model methods, and Gabor based on transformation methods. The LBP feature can reflect the microstructure among pixels, and the improved LBP feature has the advantages of rotation and translation invariance. In essence, the Gabor feature has the effect of allowing the information of a certain frequency band to pass through it, and the remaining sub \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Purpose** & **Accuracy** & **Problems** \\ \hline
[50] & 2016 & \
\begin{tabular}{c} Combining HOG feature with Support Vector \\ Machine (SVM) to identify grape leaves \\ Identifying different plant leaves on the basis of \\ improved LBP \\ \end{tabular} & 83.50\% &
\begin{tabular}{c} Single-feature detection has poor \\ stability and low accuracy. \\ \end{tabular} \\ \hline
[51] & 2018 & \
\begin{tabular}{c} Using three shape features to compare the effect of \\ SVM or Artificial Neural Network (ANN) on \\ detecting sugar beets and weeds \\ Combining GW (Gabor wavelet) and GFD (gradient \\ field distribution) to classify different weeds \\ \end{tabular} & 93.33\% &
\begin{tabular}{c} Analysis on the selection of \\ features is lacking. \\ \end{tabular} \\ \hline
[53] & 2015 & \
\begin{tabular}{c} Combining Gabor and Grey-level Co-occurrence \\ Matrix (GLCM) to classify 31 plant leaves \\ Extracting the shape and texture features of an \\ image to classify and recognize plant leaves \\ \end{tabular} & 91.60\% &
\begin{tabular}{c} No actual field images are included, \\ and the dataset is only composed of \\ different plant leaves, without \\ complex background, such as soil. \\ \end{tabular} \\ \hline
[55] & 2015 & \
\begin{tabular}{c} Using improved LBP and GLCM to categorize fresh \\ tea in the production line \\ \end{tabular} & 94.80\% &
\begin{tabular}{c} Nonwhole plants are detected and \\ recognized, and only the same kind of \\ leaves is classified. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research status and problems of traditional machine learning methods. information is filtered out. GLCM usually contains 10 statistics, which can reflect the spatial correlation of gray values of any two points in an image. GGCM considers the gradient information on the basis of GLCM, and it mainly has 15 statistics. The fractal dimension uses the self-similarity between local and whole research objects, and its methods include \"blanket\" algorithm, fractal Fourier, and box-counting dimension [61]. A large amount of texture information in crop and weed leaves plays an important role in recognition and classification tasks [62]. For example, [PERSON] et al. [63] extracted 52 texture features (GLCM features in four directions) from wavelet multiresolution images for weed segmentation. [PERSON] et al. [52] used the combination of Gabor wavelet (GW) and gradient field distribution (GFD) to extract a new feature vector set based on directional texture features to classify weed species. [PERSON] et al. [64] constructed a method based on texture feature extraction, which extracts texture features from field images composed of wide and narrow leaf weeds.