# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 4 Discussion  
In this study, it was found that the accuracy of SPAD value prediction was enhanced by using color information (e.g., RGB, HSL, and HSV) obtained from digital images and photographic environment information (color temperature and illuminance) as the model input. Previous studies have proposed methods to stabilize the light environment during photographic [18] and color-corrected images using color charts as color standards [28]. However, a method to stabilize the light environment at the time of photography can  
Figure 6: Partial dependence and individual conditional expectations in the best model for SPAD value prediction with Random Forests using RGB values in halogen and/or LED light sources: (**a**) HAL+LED_RGB_G_mean, (**b**) HAL+LED_RGB_G_median, (**c**) HAL+LED_RGB_G_mu, (**d**) LED_RGB_B_median, (**e**) LED_RGB_B_mean, (**f**) LED_RGB_B_mu, (**g**) LED_HSL_H_median, (**h**) LED_HSL_H_mean, and (**i**) LED_HSL_S_mean. only predict SPAD values on an individual leaf basis, although it can provide a highly accurate SPAD value prediction. In addition, the color correction of an image is often difficult under certain conditions. In contrast, our method presented the potential of the sensor fusion of a color meter or an illuminance sensor for image-based SPAD value prediction that can be applied for a UAV [7; 8; 29] or fixed camera [30; 31] without limiting the photographic conditions. No significant decrease in prediction accuracy was observed when the data obtained under both halogen and LED light sources were combined for training. However, the SPAD value prediction by the HAL model was less accurate than the LED model. This suggests that the color temperature affects the image-based SPAD value prediction using Random Forests. The result also suggests that SPAD values can be predicted even under non-white conditions, such as dusk, by collecting and learning data under different illuminance conditions in various light source environments. The high accuracy of the SPAD value prediction for the LED model suggests that the LED light source is a more suitable as a light source for image-based SPAD value prediction. However, if the object is in shadow, the SPAD value prediction may be difficult, or the error in the SPAD value prediction may be large. Therefore, it is necessary to consider a method that uses the ratio of shadows as a model input or a color correction method for acquired images when shadows exist on an object [31; 32]. The SPAD value is used as an index for environmental control and as a guideline for harvests when producing crops. Although this study focused on radiishes, the proposed experimental method can be applied to other crops as well. However, it is unclear whether the model performance can be higher for different varieties or crops other than radiishes, as the leaf colors and SPAD values are different among cultivars. Therefore, a direct comparison cannot be examined, because the radish cultivar used in this study is unknown, as it was purchased at a supermarket. Thus, the application of this study to other cultivars and crops is a future challenge. Following a previous study where [8] reported the results of a similar analysis excluding the R value, which is a wavelength absorbed by water molecules, we conducted modeling using only GB values, excluding the R values in this study. As a result, the GB-based model also showed high accuracy, with a COR of 0.85 or higher. However, the GB-based model showed a lower model performance than the RGB-based models. This suggests that the GB-based models can exclude the effect of water molecules, but the R values may contain important factors for the SPAD value prediction. The HSL value obtained under the LED light source, which produced models with the highest model performance in this study, is converted using RGB values. Therefore, the color component may be affected by water molecules in the leaves and may not be an appropriate color space when considering the effect of water molecules in the leaves.