## 5 Conclusions  
In this study, a prediction model of rice biomass based on the fractal dimension was proposed. We obtained the image of rice through the camera and extracted the phenotypic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Growth Stage** & \(R^{2}\) & _MAPE_ \\ \hline The first stage & 0.8358 & 5.27\% \\ The second stage & 0.8067 & 5.55\% \\ The third stage & 0.9196 & 4.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: \(R^{2}\) and \(MAPE\) for the linear regression plant height model.  
characteristics of rice. The fractal dimension of the rice image was calculated by the box-counting method. Based on the combination of traditional characterization and the fractal dimension, prediction models were established by using random forest, SVR and multiple linear regression to predict the dry weight, fresh weight and plant height of rice. The model achieved an excellent prediction effect. We can draw the following conclusions.  
As an image feature of rice, the fractal dimension can be used to predict the biomass of rice. According to the image and correlation coefficient, the fractal dimension is correlated with dry weight, fresh weight and plant height of rice. The fractal dimension can improve the effect of the biomass prediction model. Therefore, the fractal dimension can be considered to achieve better results in future research on rice.  
By using various methods to establish the biomass prediction model of rice, it can be found that the effect of the linear regression model is better than random forest and SVR. Compared with the random forest model and the SVR model, the multiple linear regression model has the largest \(R^{2}\) and the smallest \(MAPE\), and has a good explainability. Therefore, when using rice images to build models to predict dry weight, fresh weight and plant height, multiple linear regression models are preferred. In addition, random forest and SVR are not recommended.  
Although the growth stage of rice will affect the prediction effect of the model, the fluctuation is within the acceptable range. Compared with the models established above, only the SVR dry weight model shows a great decline in \(R^{2}\). The \(R^{2}\) of the other models can remain above 0.8. Although the stage of rice growth has changed, the \(MAPE\) of these models remain stable. The overall effects of these models are still quite excellent.  
Conceptualization, Y.H. and J.S.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and Y.H.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and Y.Q.; data curation, [PERSON]; writing--original draft preparation, ALL; writing--review and editing, [PERSON] and J.S.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Strategic Science and Technology Development Fund (Grant No. G20200017074), National High-End Foreign Expert Introduction Program, Master Studio of Huazhong Agricultural University, National Natural Science Foundation of China (Grant No. 31701317), Fundamental Research Funds for the Central Universities (Grant No. 2662020 GXYY007), Science and Technology Innovation Fund of Huazhong Agricultural University (Grant No. 2021367) and National Innovation and Entrepreneurship Training Program for College Students (Grant No. S202010504228).  
Collected data are available from the authors. The codes are stored in [[https://github.com/huyijunhenshuai/rice-prediction.git](https://github.com/huyijunhenshuai/rice-prediction.git)]([https://github.com/huyijunhenshuai/rice-prediction.git](https://github.com/huyijunhenshuai/rice-prediction.git)) access on 17 June 2021.  
The authors would like to thank the three anonymous referees for their constructive comments and suggestions. Thanks to [PERSON] for her support and guidance in this research.  
The authors declare no conflict of interest.