# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods  
### Deep Learning-Based Plant Disease Detection Technology  
Plant disease detection technology uses computer vision technology to detect plant disease-infested areas and their exact locations under complex natural conditions, which is a prerequisite for the accurate classification and identification of plant diseases and the assessment of disease damage levels.  
Early plant disease algorithms used a sliding window strategy to select candidate regions, then extract candidate region features, and, finally, using a classifier, classify them to obtain the target regions. This method traverses the image by setting different scales and widths. Although this method does not miss any disease region target, the ensuing redundant candidate windows bring great computational effort and it is very time-consuming to traverse the disease image all over again, resulting in poor real-time detection [21].  
With the rapid development of artificial intelligence technology, different techniques are based on artificial vision for digital image processing and the implementation of the image classification model. Reference [22] proposed methodology that consists of five stages, as shown in Figure 1, i.e., image acquisition, preprocessing, segmentation, feature extraction, and classification, to find the damages caused by the cogollero worm in corn fields.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plant Type** & **Dataset** & **Strength** & \
\begin{tabular}{c} **Detection Network** \\ **Framework** \\ \end{tabular} & **References** & **Year** \\ \hline Paddy crops & 1500 & Better accuracy & Mask R-CNN & Anandhan, Singh, etc. [13] & 2021 \\ \hline Sugarcane & 2940 & Higher accuracy & Faster-RCNN & Kumar [14] & 2021 \\ \hline \multirow{3}{*}{Papaya} & \multirow{3}{*}{2000} & Propose the use of lighter & \multirow{3}{*}{YOLO} & \multirow{3}{*}{Maski, Thondiyath [15]} & \multirow{3}{*}{2021} \\  & & versions of YOLO which are & & & \\  & & more efficient and have high & & & \\  & & detection speed & & & \\ \hline Citrus & 392 & Higher accuracy & YoLov4 & Li Hao, etc. [16] & 2021 \\ \hline Apple & 1200 & Better accuracy & Mask RCNN & Rehman etc. [17] & 2021 \\ \hline Grape & 4500 & Better accuracy & GLDDN & Dwivedi, etc. [5] & 2021 \\ \hline Tomatoes & 2385 & Efficient and precise & MobileNetv2-YoLov3 & Liu and Wang [18] & 2020 \\ \hline Grape & 4449 &
\begin{tabular}{c} Higher accuracy and a \\ satisfactory detection speed \\ \end{tabular} & Faster DR-IACNN & Xie, etc. [19] & 2020 \\ \hline \multirow{3}{*}{Cassava} & \multirow{3}{*}{2415} & Deploy the model in a & \multirow{3}{*}{SSD} & \multirow{3}{*}{[PERSON], etc. [20]} & \multirow{3}{*}{2019} \\  & & mobile application and test & & & \\ \cline{1-1}  & & its performance on mobile & & & \\ \cline{1-1}  & & images and video & & & \\ \hline \multirow{3}{*}{Tea} & \multirow{3}{*}{4000} & Identify an accurate yet & \multirow{3}{*}{YOLOv3} & \multirow{3}{*}{[PERSON], etc. [8]} & \multirow{3}{*}{2019} \\ \cline{1-1}  & & efficient detector in terms of & & & \\ \cline{1-1}  & & speed and memory & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related work on plant disease detection.  
Moreover, many deep learning-based methods can be applied to plant disease diagnosis, as shown in Figure 2.