Speaker
Description
Plant phenotyping plays a crucial part in the development of new crop genotypes. In recent years, new digital phenotyping technologies have emerged, especially in the context of high throughput field phenotyping (Furbank and Tester, 2011). Many of these methods need expensive equipment or depend on stationary phenotyping platforms (e.g. Kirchgessner et al., 2016). Relatively simple and easy to use methods need to be developed if benefits of new digital technologies are to be transferred to daily use in variety testing or breeding. In this study, the applicability of relatively simple commercially available digital phenotyping devices was tested and improved in the context of wheat variety testing. Aerial thermography is used to evaluate the performance of genotypes by measuring canopy temperature (CT) as a low CT is indicative of the relative fitness of a plant to the environment (Reynolds et al., 2012). Because lightweight thermal cameras for drones are prone to significant thermal drift effects due to a lack of a signal stabilizing cooling (Wang et al., 2023), we propose a new approach to analyze drone based thermal images based on an image-wise method described in Roth et al. (2018). Through the inclusion of covariates such as trigger timing and the position of the drone relative to measured plots, temporal trends and viewing-geometry related effects could be mitigated, which improved the CT measurements. Correlations between measurements on 270 experimental wheat plots taken within 20 min were very strong (R = 0.99) and highly genotype specific with generalized heritabilities > 0.95 in many cases. In a second experiment, autonomous PhenoCams mounted on poles 12 m above the field were evaluated for their suitability to track later stages of phenology (BBCH) and senescence (% of senescence progression on leaf- and plant-level) as a replacement for time consuming manual field scorings. Senescence and maturity of wheat could be tracked reliably in the field for three subsequent seasons with strong correlations between field-scorings and image-based estimates (R > 0.8) in a 100-fold cross-validation of a PLSR-based model (Figure 1). For emergence, achieved correlations were poor. Both experiments demonstrated how image-based phenotyping with a simple and affordable setup can be used to derive high quality data relevant in the evaluation of the performance of wheat genotypes in the field.
Figure 1: Pearson correlation coefficients between manual field reference measurements and PhenCam based estimates of the timing of phenological stages (BBCH; BBCH values on the x axis), senescence on leaf level (SenLeaf; % of senescence on the x axis) and senescence on plant level (SenPlant; % of senescence on the x axis). The blue shaded area indicates the minimal, and maximal correlation achieved in a 100-fold cross validation based on a random 75%- 25% split into training and testing data. The blue line indicates the mean. Correlations when applying the whole dataset are shown in red.
References:
Furbank, R.T., Tester, M., 2011. Phenomics - technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644. https://doi.org/10.1016/j.tplants.2011.09.005
Kirchgessner, N., Liebisch, F., Kang, Y., Pfeifer, J., Friedli, M., Hund, A., Walter, A., 2016. The ETH field phenotyping platform FIP: A cable-suspended multi-sensor system. Functional Plant Biology 44, 154–168. https://doi.org/10.1071/FP16165
Reynolds, M.P., Pask, A.J.D., Mullan, D.M., 2012. Physiological breeding I: interdisciplinary approaches to improve crop adaptation. CIMMYT.
Roth, L., Aasen, H., Walter, A., Liebisch, F., 2018. Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography. ISPRS Journal of Photogrammetry and Remote Sensing 141, 161–175. https://doi.org/10.1016/j.isprsjprs.2018.04.012
Wang, Z., Zhou, J., Ma, J., Wang, Y., Liu, S., Ding, L., Tang, W., Pakezhamu, N., Meng, L., 2023. Removing temperature drift and temporal variation in thermal infrared images of a UAV uncooled thermal infrared imager. ISPRS Journal of Photogrammetry and Remote Sensing 203, 392–411. https://doi.org/10.1016/j.isprsjprs.2023.08.011
| Keywords | plant phenotyping, aerial thermography, canopy temperature, wheat, thermal drift, phenology, senescence, repeated RGB images |
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