Abstract
With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.
Highlights
Despite numerous advances in the field of genetics and the application of new molecular technologies in plant research [1], the genetic gain of major crops has stabilized or even stagnated in many regions of the world [2,3]
The correlations of HEICHM and height measured in the field (HEIGT) were slightly below the correlations observed in similar studies [48,105,106], which could be due to the fact that the plant height showed a relatively small variation when using plant growth regulators
Remote sensing from unmanned aerial vehicles (UAV) is expected to be an important new tool to assist breeders and farmers in precision agriculture, but there has only been a slow adoption of promising
Summary
Despite numerous advances in the field of genetics and the application of new molecular technologies in plant research [1], the genetic gain of major crops has stabilized or even stagnated in many regions of the world [2,3]. While there are a variety of technical options to collect data in the field, satellites [9,10,11,12], stationary in-field sensors [13] or ground-based vehicles and farm robots [14,15,16] always have a catch and enforce decision making about flexibility, scalability and the desired data precision. The regulatory take-off weight and the technically possible payload [17] pose a limit to what flying platforms can carry, Unmanned Aerial Vehicles (UAVs) provide the possibility for high-resolution multispectral and even hyperspectral imaging that can still cover a mid-size area with an acceptable spectral, spatial and temporal resolution [18]. UAVs can be relocated to spatially distributed plots with less effort and almost no time-loss. They are the closest solution to a semi-continuous field survey
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