# A Digital Framework to Predict the Sunshine Requirements of Landscape Plants
## 4 Conclusions and Outlook  
In this paper, a digital framework has been proposed to predict the sunshine requirements of landscape plants, combined with the digital simulation for solar radiation and some equipment. The results could be used for plant selection and configuration based on site adaptability, and could act as a basic reference for the practice of urban greening and sustainable urban forest management. The main innovation of this research is the introduction of a health standard system for landscape plants based on black-box thinking and digital technology. When combined with LI-6400 XT validation, the results indicate that the digital framework has a good prediction accuracy and stability. From the comparison with the results of other scholars, we can see that the healthy level as an indicator used to test the solar radiation response of plants has more practical value than the LCP indicator, especially for the application of information technology in the future, such as intelligent decision support systems. The biggest challenge for our research is to find more plant species and samples in different geographic latitudes and climate regions, and obtain their values of healthy level for sunshine requirements. In this study, we have predicted the sunshine demand values of 15 landscape plant species in the city of Wuhan, and proposed optimization strategies based on the site types, the annual total solar radiation, and growth status of plant (Figure 7). Under our technical guidance and suggestions, some dead landscape plants due to inappropriate sunshine in study areas, have been replaced with more adaptable plant species (Figure 8). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scientific Name**} & \multicolumn{4}{c}{**LCP (\(\mu\)mole/m\({}^{2}\)-s)**} & \multicolumn{2}{c}{**Data of this Research [72]**} \\ \cline{2-7}  & **Data-1 [44]** & **Data-2 [46]** & **Data-3 [43]** & **Data-4 [45]** & **Sub-Healthy** & **Healthy** \\ \hline _Fatsia japonica_ (Thunb.) Decne. et & & & & & & \\ [PERSON]. & & & & & & \\ _Loropetalum chinese_ var. rubrum & & & & & & \\ [PERSON] & — & & & & & \\ _Rhododendn simsii_ [PERSON]. & & & & & & \\ _Euonymus japonicus_ var. & & & & & & \\ alba-marginata [PERSON]. [PERSON] & & & & & & \\ _Nandina domestica_ Thunb. & & & & & & \\ _Mahonia fortunei_ ([PERSON].) Fedde & & & & & & \\ _Cercis chimetis_ Bunge & & & & & & \\ _Aucuba japonica_ var. variegata & & & & & & \\ D’Om-Brain & & & & & & \\ \hline \hline \end{tabular} Note: ‘LCP’ is the abbreviation of light compensation point, and the symbol ‘—’ indicates no data.  
\end{table} 
Table 5: Comparison between the results of our research and studies of other scholars. In natural planting sites, such as residential areas, the number of plant samples is often limited by many factors, which can affect the accuracy of the data fitting between the health responses of plants and solar radiation. Soil moisture is a very important factor for plant health. The reality is that our study cannot control this factor, to ensure absolute consistency for soil moisture. However, as a residential area with the complete irrigation facilities, we can assume the same soil conditions for study areas, especially in the case of a sufficiently large number of samples. In addition, we also found that the practical value of this research is greater in high latitude regions, because of the great changes in the solar altitude angle in a year. In our future research, we will collect more plant samples in similar geographic locations in built-up areas, and we will attempt to more accurately predict the healthy, sub-healthy, and unhealthy levels of a specific plant species. Furthermore, the solar radiation model reveals the distribution of plant species and plant diversity in urban built-up areas, which needs to be further explored.