## 4 Discussion  
Table 4 studies the different fruit detection and counting methods applied to citrus. It compares the task, datasets, and methods and lists the advantages and disadvantages of each to better explain the experimental results obtained in this paper.  
In this study, we found that due to the complex citrus orchard environment and the biological characteristics of leafy citrus tree, the tracking and counting of citruses were difficult. According to the significant color difference between the citrus fruits and the orchard background, [PERSON] et al. [26] used the K-Means clustering algorithm and the Gradient-based Hough transform to count the number of fruits in a single citrus tree based on static images. The traditional method is simple but easily affected by the weather and other natural environments, which results in fewer practical applications.  
Compared with traditional methods, deep learning often uses trained algorithms for target detection and tracking. [PERSON] et al. [1] improved citrus fruits recognition by the incorporation of the SE attention mechanism and combined DeepSORT algorithm with improved YOLOv5. [PERSON] et al. [27] improved the YOLOv4 by using CSPResNest50 network and the Recursive Feature Pyramid network. These YOLO algorithms are better than traditional but not as effective as YOLOv7-tiny.  
Considering the practical applications, [PERSON] et al. [17] achieved the detection and automatic estimation of citrus with a lightweight model for edge computing devices and [PERSON] et al. [28] used the [PERSON]'s circle transform method on MATLAB software (v. 2017b) to achieve rapid automatic fruit detection and yield prediction. These approaches are difficult to port to embedded devices and require specialized edge computing devices.  
In this study, we proposed a citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, the Kalman filter tracking and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Reference** & **Task** & **Dataset** & **Methods** & **Pros and Cons** \\ \hline \multirow{4}{*}{2023} & \multirow{4}{*}{[1]} & Detection and & \multirow{4}{*}{Smartphone; 7272} & \multirow{4}{*}{YOLOv5 model with SE} \\  & & counting of citrus & & from a citrus \\  & & fruits in a & & plantations in \\  & & greenhouse & & \\  & environment. & & China. & \\ \hline \multirow{4}{*}{2023} & \multirow{4}{*}{[17]} & Detection and & \multirow{4}{*}{Smartphone; 40 videos} & \multirow{4}{*}{CSPDarkNet53+PAFPN} \\  & & automatic & & \\  & & estimation of citrus & & from a citrus \\  & & with a lightweight & plantations in & \\  & & model for edge & & \\  & & computing devices & & \\ \hline \multirow{4}{*}{2022} & \multirow{4}{*}{[26]} & Counting the & \multirow{4}{*}{MER-131-U3C} \\  & & number of fruits in & Industrial Camera; \\  & & a single citrus tree & & \\  & & based on static & & \\  & & images. & & \\ \hline \multirow{4}{*}{2021} & \multirow{4}{*}{[27]} & Detection and & \multirow{4}{*}{Camera; 900 photos and some} & \multirow{4}{*}{YOLOv4, CSPResNest50} \\  & & counting of & & \\  & & Kasumi citrus & videos from Chengdu & \\  & & city, China. & \\ \hline \multirow{4}{*}{2019} & \multirow{4}{*}{[28]} & \multirow{4}{*}{Rapid automatic} & Smartphone iPhone \\  & & & XR; \\  & fruit detection and & Dataset from Shiraz \\  & & & [PERSON]’s circle transform \\  & & & \\  & & & California, USA & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Studies on different fruit detection and counting methods applied to citrus.  
the Hungarian algorithm. The YOLOv7 has a fast detection speed, a high target detection accuracy, a lightweight network and easy portability to various embedded devices. The incorporation of the two stages life filter makes DeepSORT have high tracking efficiency and counting accuracy.