The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

Abstract

Background The advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop’s condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements. Results The results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The $$r^2$$ r 2 of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The $$r^2$$ r 2 of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition. Conclusions Our work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

Impacts of Variable Illumination and Image Background on Rice LAI Estimation Based on UAV RGB-Derived Color Indices

“…Finally, the image is transformed back to the spatial domain to obtain a final enhanced image. The study by Wang et al [23] contains the specific steps and parameter settings.…”

“…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].…”

“…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].…”

A Novel Method for Optimizing Color Selection Using the Hadamard Product Technique

“…UAVgenerated high-resolution images and computer vision algorithms directly influence yield. Automatic multiresolution Retinex correction reduces lighting effects in color images, enhancing multivariate linear models' performance [26]. Fundus images can be corrected for uneven contrast and luminosity using a five-step process: image input, selection, luminosity correction, histogram stretching, and postprocessing [27].…”

Correction: The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

“…In the original publication of the article [ 1 ], the author’s name Gert Kootstra was incorrectly written as Kootstra Gert. The original article has been corrected.…”