# Review of Weed Detection Methods Based on Computer Vision
## 3 Traditional Machine Learning Weed Detection Methods  
In the early stage, many scholars used machine learning algorithms combined with image features to conduct weed recognition tasks, achieving the purpose of weed detection. These traditional ML methods require a small sample size and short training time; they also have a low requirement for graphics processing units. They can be used in agricultural machinery and equipment at a low cost, providing an effective method and approach for realizing plant identification and weed detection based on image-processing technology. These intelligent technologies rely on the continuous development of machine vision technology. Machine vision technology uses a series of image-processing methods to extract the shallow features of weeds and then sends them to a classifier for detection. Initially, crops or weeds are identified by calculating the texture, shape, color, or spectral features of images. For example, [PERSON] et al. [38] realized the distinction between corn and single species of weeds on the basis of Local Binary Pattern (LBP) texture features and SVM. [PERSON] et al. [39] proposed a multi-feature weed reverse location method in a soybean field on the basis of shape and color features. [PERSON] et al. [40] proposed a classification method for five kinds of weeds in farmland on the basis of shape and texture. [PERSON] et al. [41] conducted a comparative analysis of the gray distribution of each component in the color space of RGB, HSV, and HIS of common weeds in a field at the pea seedling stage. They proposed a method for weed segmentation and extraction in complex background based on R-B color difference features. Some scholars have used plant height [42] or location information [43; 44; 45] to improve the identification accuracy, but these methods are easily affected by vibration or other uncontrolled motion in practical application [46]. Moreover, some research has focused on using a single feature to identify plants, which has low accuracy and poor stability. To deal with the problems of a complex field environment and the low accuracy and poor stability of a single feature, some scholars have also proposed to integrate multiple features to improve the accuracy. For instance, [PERSON] et al. [47] integrated multisource recognition information of different features, such as plant leaf shape, fractal dimension, and texture. They combined the good classification and promotion capabilities of SVM in the case of small samples and the advantages of Dempster-Shafer evidence theory of incomplete and uncertain information. Compared with single-feature recognition, this multi-feature decision fusion recognition method has better stability and a higher recognition accuracy. [PERSON] et al. [5] proposed a machine vision prototype based on video processing and meta-heuristic classifiers based on Gray-level Co-occurrence Matrix (GLCM), color feature, texture feature, invariant moment, and shape feature. They used them to identify and classify 4299 samples from potatoes and five weed species online, achieving high accuracy. [PERSON] et al. [48] integrated the color, shape, and texture features of weed images with a total of 101-dimensional features to solve the problem of the low recognition accuracy of a single feature of weeds in a rice field. [PERSON] et al. [44] used a combination of vertical projection and linear scanning in corn farms under different lighting conditions to identify the centerline of crop rows. This method only recognizes crop rows, all plants among rows are identified as weeds regardless of their type, and it is unsuitable for identifying different types of weeds. On the whole, these studies have provided effective methods and approaches for realizing plant recognition and weed detection based on image-processing technology in the early stage. However, most of the studies are only for the identification of different plant leaves rather than the precise detection of crops or weeds in a field.