###### Abstract  
Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation. variable illumination; image background; color indices; Retinex correction; image segmentation +
Footnote †: journal: Medical  
result in limited estimation accuracy [16]. Therefore, machine learning-based methods are commonly utilized to improve the estimation of LAI [10; 17; 18]. However, variation in illumination poses a significant challenge to the application of UAV imagery [19]. Variation in cloud cover and the inherent structural characteristics of crops are primary influencing factors on the variability in illumination [20; 21; 22]. The literature indicates that variation in illumination can significantly alter RGB images captured by UAV, hence affecting the application of CIs [23]. Therefore, it is imperative to perform illumination correction on the RGB images acquired from UAV [23]. However, existing research, particularly regarding the application of RGB-derived CIs, has shown limited consideration of methods such as light correction for reducing the impact of illumination variation on CIs [24]. This has constrained the accuracy of estimating crop phenotypic information based on CIs [25]. Therefore, color calibration is necessary to obtain reliable results [21]. Based on a recent literature review, only a minority of studies have employed color correction [25]. Thus, researchers have considered alternative approaches to overcoming the impact of variation in illumination by integrating texture, shape, and other features [26; 27]. However, these approaches are unstable for many types of crop scenarios, including rice images obtained in complex field environments [26]. When studying crop phenotypes using CIs, another often-overlooked factor is the background of images, as this often limits the application of CIs [28]. The background elements of an image can be extracted through image segmentation [29; 30]. One common method for crop image segmentation is to use CIs to separate crop and background by setting thresholds using different methods [31] that leverage the significant difference in CIs between crop and background pixels [29]. However, calculating CIs based on the average of the entire set of image pixels, rather than pixels of the crops, will inevitably lead to inaccurate results for the CIs, thereby causing overestimation or underestimation of phenotypic information [32; 33]. For example, [PERSON] et al. [12] discovered that a soil background heavily influenced the accuracy of maize LAI estimation. In a paddy field, apart from the soil, background elements may also include water, shadows, and weeds, which would interfere with the estimation of rice LAI [34]. Moreover, the background content of rice paddy images varies during different growth stages. During the filtering stage, the background elements include water, soil, weeds, and shadows [35; 36]. In studies performed after the booting stage when rice plants have grown taller and formed a canopy, the background of the images has not been considered [31].