Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination

Abstract

BackgroundOne of the main challenges associated with image-based field phenotyping is the variability of illumination. During a single day’s imaging session, or between different sessions on different days, the sun moves in and out of cloud cover and has varying intensity. How is one to know from consecutive images alone if a plant has become darker over time, or if the weather conditions have simply changed from clear to overcast? This is a significant problem to address as colour is an important phenotypic trait that can be measured automatically from images.ResultsIn this work we use an industry standard colour checker to balance the colour in images within and across every day of a field trial conducted over four months in 2016. By ensuring that the colour checker is present in every image we are afforded a ‘ground truth’ to correct for varying illumination conditions across images. We employ a least squares approach to fit a quadratic model for correcting RGB values of an image in such a way that the observed values of the colour checker tiles align with their true values after the transformation.ConclusionsThe proposed method is successful in reducing the error between observed and reference colour chart values in all images. Furthermore, the standard deviation of mean canopy colour across multiple days is reduced significantly after colour correction is applied. Finally, we use a number of examples to demonstrate the usefulness of accurate colour measurements in recording phenotypic traits and analysing variation among varieties and treatments.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0308-5) contains supplementary material, which is available to authorized users.

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

“…The calibration target was attached to the base of the platform such that it was always visible from the perspective of one camera as described in Appendix C. Colour calibration was performed on all images according to the method proposed in [41]. Field imaging was carried out between 23 September 2016 and 18 November 2016 inclusive (see Table 2).…”

“…On the other hand, remote images of the field are captured with sensors attached to aerial platforms [36][37][38][39] for trait estimation. Recent studies to quantify plant canopy development from images either report trait comparisons with reference to a different sensor technology such as LiDAR [16,40] or compare image-based estimation techniques with manual methods [5,41]. A comparison of the performance of two imaging methods on the same field study has hitherto not been reported previously.…”

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

“…It was also noted that the color corrected images had consistently higher 385 green intensity than red and blue due to the high chlorophyll content at the seedling stage. While at 386 the senescence stage, the red value is expected to be increased, as plants begin to turn yellow with 387 decreased chlorophyll content (data was not shown here) [55]. It would be very difficult to observe Our study focused on improving the efficiency of the color calibration process using a deep learning-based method and demonstrating its applicability in real-time series data and did not consider its implications and usefulness for typical plant phenotyping analyses since this was demonstrated in several studies [55,56].…”

“…While at 386 the senescence stage, the red value is expected to be increased, as plants begin to turn yellow with 387 decreased chlorophyll content (data was not shown here) [55]. It would be very difficult to observe Our study focused on improving the efficiency of the color calibration process using a deep learning-based method and demonstrating its applicability in real-time series data and did not consider its implications and usefulness for typical plant phenotyping analyses since this was demonstrated in several studies [55,56]. For example, color-corrected images are useful for detecting the variations among different plant varieties or plants grown under different nitrogen concentrations, and even plants grown under inconsistent illumination conditions [57].…”

“…It was also noted that the color corrected images had consistently higher 385 green intensity than red and blue due to the high chlorophyll content at the seedling stage. While at 386 the senescence stage, the red value is expected to be increased, as plants begin to turn yellow with 387 decreased chlorophyll content (data was not shown here) [55]. It would be very difficult to observe 388 such chlorophyll-related changing patterns of plants from RGB images without color calibration.…”