## 3 Experiments and Analysis
### Results and Analysis
#### 3.3.3 Fruit Counting Accuracy  
In order to accurately estimate the number of citruses in the video and realize the counting of citrus to estimate the production, we propose a drawing lines counting strategy to count citrus and conduct experiments on the citrus video dataset. We compare the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NO** & **Legend (of a Map, etc.)** & **Condition** & **Influence** \\ \hline
4 & & & & \\ \hline
5 & & & & \\ \hline
6 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Before Improvement**} & **After Improvement** & **Results** \\ \cline{2-5}
**Performance** & & & & \\ \hline Video detection & Normal conditions & 95.12\% & 95.12\% & No improvement \\ \cline{2-5} accuracy & obscured citrus & Same as above & Same as above & No improvement \\ \hline \multirow{2}{*}{ID switch rate} & Normal conditions & 25.97\% & 15.63\% & Improvements \\ \cline{2-5}  & obscured citrus & Same as above & Same as above & Improvements \\ \hline Multiple objects & Normal conditions & 45.36\% & 67.14\% & Improvements \\ \cline{2-5} tracking accuracy & obscured citrus & 5\% decrease & Not affected & Improvements \\ \hline Multiple objects & Normal conditions & 56.77\% & 74.65\% & Improvements \\ \cline{2-5} tracking precision & obscured citrus & 7\% decrease & Not affected & Improvements \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of tracking results before and after algorithm improvement. manual counting values, the algorithm's counting values before improvement, and the algorithm's counting values after improvement, then validate the effectiveness of the algorithm by fitting the manual counting values and the algorithm's counting values after improvement. The fitted straight line is shown as Formula (12) and \(R^{2}\) (The coefficient of determination) is 0.9982, \(RMSE\) (Root Mean Square Error) is 32.300. \[y=1.0742\ x+0.8083 \tag{12}\]  
The \(RMSE\) (root mean square error) between the number of fruits obtained by the algorithm and the number of fruits obtained by manual counting is 32.300. the counting accuracy of the 10 citrus videos based on Equation (11) was about 80%, with an average accuracy of 81.02%. From the fitting results, the coefficient of determination \(R^{2}\) value is 0.9982, indicating that the algorithm in this paper has a significant linear correlation between the fruit counting values in modernorchard videos and the real values. From the fitting results, it can be seen that the counting accuracy does not change significantly with the number of citruses in the video, indicating that the method proposed in this paper has good robustness. The comparison of counting values is shown in Figure 5. Figure 5 shows that the algorithm counting values after improvement are closer to the real values of manual counting than the algorithm counting values before improvement. To test whether there is a significant difference between the algorithm counting values after improvement and manual counting value, we used the \(p\)-value test of the chi-square test. We take the manual counting value as the target value and the algorithm counting values after improvement as the actual value. Then we formulate the original hypothesis that there is no significant difference between the algorithm counting value and the manual value, and the alternative hypothesis that there is a significant difference between the algorithm counting value and the manual value. Finally, the statistic comes to be tested by the \(p\)-value. The \(p\)-value is the probability of the chi-square statistic, which indicates the probability that the observed chi-square statistic is greater than or equal to the actual observed chi-square statistic if the original hypothesis holds. If the \(p\)-value is less than the significance level, the original hypothesis is rejected and the alternative hypothesis is accepted, i.e., the algorithm counting value is significantly different from the manual value.