# Review of Weed Detection Methods Based on Computer Vision
## 2 Public Image Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Datasets** & **Purpose** & **Plant** & **Image Size** & **Features** \\ \hline
[18] & \begin{tabular}{c} perennial rygrass \\ and weed \\ \end{tabular} & \begin{tabular}{c} \\ Weed detection and control \\ \end{tabular} & \begin{tabular}{c} Dandelion, ground ivy, \\ spotted spurge, and ryegras \\ \end{tabular} & \
\begin{tabular}{c} 1920 \(\times\) 1080 \\ 33,086 \\ \end{tabular} &
\begin{tabular}{c} It includes 17,600 positive images (contain target words) and \\ 15,486 negative images (contain perennial rygrass with no \\ target vessels). \\ \end{tabular} \\ \hline
[19] & \begin{tabular}{c} Grass-Broadleaf \\ \end{tabular} & \begin{tabular}{c} Weed detection by using \\ ConvNets \\ \end{tabular} & \begin{tabular}{c} Soil, soybean, broadleaf, and \\ grass weeds \\ \end{tabular} & \
\begin{tabular}{c} 4000 \(\times\) 3000 \\ 15,336 \\ \end{tabular} &
\begin{tabular}{c} Data are from a set of images captured using a UAV and the \\ SLLC algorithm. These images are segmented, and the \\ segments are annotated manually. The ratio of soil: soyhens: \\ grass: broadleaf is roughly 373-3731 (Figure 1a). \\ \end{tabular} \\ \hline
[20] & \begin{tabular}{c} Plant seedlings \\ dataset \\ \end{tabular} & \begin{tabular}{c} Identifying plant species and \\ weeding in the early growth stage \\ \end{tabular} & \begin{tabular}{c} 12 weed and crop species of \\ Danish arable land \\ \end{tabular} & \
\begin{tabular}{c} 5184 \(\times\) 3456 \\ 407 \\ \end{tabular} &
\begin{tabular}{c} Each image is provided with an ID and associated with a \\ single species. The dataset contains a full image. \\ automatically segmented plants, and single plants that are \\ not segmented. \\ \end{tabular} \\ \hline
[21] & \begin{tabular}{c} DeepWeeds \\ species based on deep learning \\ \end{tabular} & \begin{tabular}{c} Classification of multiple weed \\ species based on deep learning \\ \end{tabular} & \begin{tabular}{c} 8 nationally significant weed \\ species native to locations \\ across northern Australia \\ \end{tabular} & \
\begin{tabular}{c} 256 \(\times\) 256 \\ 17,509 \\ \end{tabular} &
\begin{tabular}{c} Each class contains between 1009 and 1125 images of the \\ corresponding species, with a total of over 800 images of \\ positive species classes. \\ \end{tabular} \\ \hline
[22] & \begin{tabular}{c} Open Plant \\ Phenotype Database \\ \end{tabular} & \begin{tabular}{c} Plant detection and classification \\ algorithms \\ \end{tabular} & \begin{tabular}{c} 47 species of common weeks \\ in Denmark \\ \end{tabular} & \begin{tabular}{c} It includes 47 different species of monocotylendous and \\ \
\begin{tabular}{c} dicolorylodethodous \\ weeds in archive crops in Denmark \\ \end{tabular} &
\begin{tabular}{c} Globalyclodethodous \\ plant species were cultivated in a semifled setting to mimic \\ natural growth conditions. \\ \end{tabular} \\ \hline
[23] & \begin{tabular}{c} WeedNet \\ \end{tabular} & \begin{tabular}{c} Dense semantic classification, \\ vegetation detection \\ \end{tabular} & \begin{tabular}{c} Crops and weeds \\ \end{tabular} & \
\begin{tabular}{c} / \\ 465 \\ \end{tabular} &
\begin{tabular}{c} Three kinds of multispectral image datasets are included: one \\ contains only 132 images of crops, the other has 243 images \\ of weeds, and the third one contains 90 images of crop-weed. \\ \end{tabular} \\ \hline
[24] & \begin{tabular}{c} Sugar beet \\ \end{tabular} & \begin{tabular}{c} Plant classification, localization, \\ and mapping \\ \end{tabular} & \begin{tabular}{c} Sugar bets and 9 different \\ types of weed \\ \end{tabular} & \
\begin{tabular}{c} 1296 \(\times\) 966 \\ \(>\)10,000 \\ \end{tabular} &
\begin{tabular}{c} Data were recorded 3 times per week until the field was no \\ proper accessible to the machinery without damaging the \\ crops. The robot carried a four-channel multispectral camera \\ and an RGB-D sensor. \\ \end{tabular} \\ \hline
[25] & \begin{tabular}{c} Rice seedlings and \\ weeds \\ \end{tabular} & \begin{tabular}{c} Image segmentation of rice \\ seedling and weeds \\ \end{tabular} & \begin{tabular}{c} Rice seedlings and weed \\ background \\ \end{tabular} & \
\begin{tabular}{c} 912 \(\times\) 1024 \\ 224 \\ \end{tabular} &
\begin{tabular}{c} The images were selected in the paddy fields, and all weeds \\ were in early growth stages. The data sample included GT \\ and RGB images (Figure 1c). \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{c} Food crops and weed identification \\ \end{tabular} & \begin{tabular}{c} Crop and weed identification \\ \end{tabular} & \begin{tabular}{c} 6 food crops and 8 weed \\ species \\ \end{tabular} & \
\begin{tabular}{c} 720 \(\times\) 1280 \\ 1118 \\ \end{tabular} &
\begin{tabular}{c} Datasets of 14 basic food crops and weeds in controlled \\ environment and field conditions at different growth stages and manually annotated images are included (Figure 1d). \\ \end{tabular} \\ \hline \end{tabular}
\end{table}