# Review of Weed Detection Methods Based on Computer Vision
## 2 Public Image Datasets

Table 1: Public weed image datasets and their features. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Datasets** & **Purpose** & **Plant** & **Image Size** & **Features** \\ \hline
[27] & Crop and weed & Instance segmentation for fine detection & Maize, the common bean, and a variety of weeds & 1200 \(\times\) 2048 & The crops include maize and the common bean. Weeds include cultivated and natural weeks. Each mask is annotated with the species name of the plant. \\
[28] & Flavia & Plant leaf classification & Leaves of 32 plants & 1600 \(\times\) 1200 & Each plant has a minimum of 50 leaves and a maximum of 77, 1907 & The background of the leaf image is white (Figure 1b). \\ \hline
[29] & CropDeep & Crop classification and testing & 30 common vegetables and fruits & 1000 \(\times\) 1000 & At least 1100 annotated samples per category and vegetables or fruits with different parts and periods of growth are included. A high degree of similarity exists among certain categories in the dataset. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. Table 2 further compares the results of different methods under the same dataset. The comparison results of the three methods are listed under each typical dataset. It can be seen that with the continuous development of the algorithm, the accuracy is getting higher and higher. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Dataset** & **Method** & **Evaluation Metrics** \\ \hline [PERSON] et al. (2018) [30] & \multirow{4}{*}{Plant seedlings dataset [20]} & AgroAVNET (A hybrid model of AlexNet and VGGNET) & Accuracy: 98.23\% \\ [PERSON] et al. (2021) [31] & & Yielding multi-fold training (YMuTf) strategy and DNN; & Accuracy: 97.18\% \\ \cline{2-3} [PERSON] et al. (2021) [32] & & Min-class-max-bound procedure (MCMB); Resnet Depthwise separable convolutional neural network, Xception & Accuracy: 99.63\% \\ \hline [PERSON] et al. (2019) [21] & \multirow{4}{*}{Deepweeds [21]} & Dataset was classified with the ResNet-50 and Inception-v3 CNN models to establish a baseline level of performance for comparison. & Accuracy: 95.1\% (Inception-v3) Accuracy: 95.7\% (ResNet-50) \\ \cline{2-3} [PERSON] et al. (2019) [33] & & Joint Unsupervised Learning of Deep Representations and Image Clusters (JULE) and Deep Clustering for Unsupervised Learning of Visual Features (DeepCluster) & Precision: 95\% \\ \cline{2-3} [PERSON] et al. (2020) [34] & & GWN (Graph Weeds Net) & Accuracy: 98.1\% \\ \cline{2-3} [PERSON] et al. (2016) [35] & & MLBP (Modified Local binary patterns) & Accuracy: 97.55\% \\ \cline{2-3} [PERSON] et al. (2021) [36] & Flavia [28] & Support vector machine with adaptive boosting MTD (multiscale triangle descriptor) and LBP-HF (local binary pattern histogram Fourier) & Precision:95.85\% \\ \cline{2-3} [PERSON] (2021) [37] & & MTD (multiscale triangle descriptor) and LBP-HF (local binary pattern histogram Fourier) & Accuracy: 99.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods under the same typical dataset. Figure 1: Four typical plant datasets: (**a**) Grass-Broadleaf database [19], with images of soybean, broadleaf weed, grass, and soil; (**b**) Flavia dataset [28]; (**c**) plant seedlings dataset [20]; (**d**) food crops and weeds dataset [26].