# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 1 Introduction  
In the cultivation of root crops such as radish and carrots, it is difficult to control the production environment while checking the condition of roots. Therefore, production environment control for root crops requires the estimation of subsurface information from aboveground information. It has been proven that chlorophyll content in leaves is related to nitrogen content in crops and can be used as an indicator to control the production environment during crop cultivation [1; 2; 3]. Conventional chemical analysis of chlorophyll content is labor-intensive, destructive, and limited to the small measurement area. In contrast, the chlorophyll value (SPAD value) measured by a chlorophyll meter (SPAD-502 Plus, Konica Minola, Inc., Tokyo, Japan) can accurately evaluate the chlorophyll content in plant leaves in a nondestructive manner. SPAD values can not only indirectly measure the nitrogen content in crops but are also related to plant growth indices and yield [3; 4; 5], and it has been reported that water stress during crop cultivation causes SPAD values to increase [6]. However, the measurement area for SPAD values using existing SPAD meters is still limited, and only one leaf can be measured at a time. Therefore, a method to predict leaf SPAD values on a large scale and in a short time would greatly contribute to the optimization of production environment control and cultivation management. With the development of color image acquisition technology, color information obtained from digital cameras is now widely used to predict the physiological characteristics of crops, such as the vegetation index (VI) [7; 8; 9], shape analysis [10], and leaf nitrogen and chlorophyll content estimation [11]. For example, VI based on the RGB color system has been integrated with crop height information and used for yield prediction [9]. HSV and HSL color systems, which are obtained by converting from the RGB color system, are also used for semantic segmentation [12], such as georeferencing and crop quality monitoring [13]. There are also studies using multispectral cameras [14; 15] and hyperspectral cameras [16; 17], but these methods require high equipment costs. In previous studies, algorithms such as Linear Regression [7], convolutional neural networks (CNN) [18], Support Vector Machines [3], Random Forests [3; 19], XGBoost [19], and Generalized Linear Models (GLMs) [20] have been applied to predict SPAD values using color values measured by images and color chromateters. Although leaf colors vary from crop to crop and direct comparisons of model performances are not possible, some algorithms have achieved high model performance in predicting SPAD values. Therefore, if we can appropriately link leaf color to SPAD values using machine learning algorithms, we can accurately estimate SPAD values from leaf color in real time. To achieve this, it is important to select appropriate input variables for each algorithm. Images acquired with a digital camera are easily affected by light sources such as brightness and color temperature [8]. Therefore, it is desirable to acquire images using digital cameras under stable light conditions. However, because the intensity of sunlight, for example, changes from moment to moment, the pixel values of an image may change spatiotemporally, even for the same subject. In response to this problem, machine learning was applied to predict SPAD values from pixel values using a photographic device that creates a stable light environment [18]. However, similar to conventional SPAD meters, this device can only measure the SPAD values of individual leaves. In agriculture, there is a need to efficiently acquire a large amount of information about crops in the fields or greenhouses. Therefore, new technologies are needed to measure SPAD values on many leaves at once. However, when measuring crops using images using UAVs for instance, it is often affected by the light environment.