The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2> 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2> 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

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The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from ~1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5° zenith angle perpendicularly to the row direction. The system is fixed to a horizontal beam supported by a tractor that moves along the micro-plots. About 100 micro-plots per hour were sampled by the system, the data being automatically collected and registered thanks to a centimetre precision geo-location. The green fraction (GF, the fraction of green area per unit ground area as seen from a given direction) was derived from the images with an automatic segmentation process and the reflectance spectra recorded by the radiometers were transformed into vegetation indices (VI) such as MCARI2 and MTCI. Results showed that MCARI2 is a good proxy of the GF, the MTCI as observed from 57° inclination is expected to be mainly sensitive to leaf chlorophyll pigments. The frequent measurements achieved allowed a good description of the dynamics of each micro-plot along the growth cycle. It is characterised by two periods: the first period corresponding to the vegetative stages exhibits a small rate of change of VI with time; followed by the senescence period characterised by a high rate of change. The dynamics were simply described by a bilinear model with its parameters providing high throughput metrics (HTM). A variance analysis achieved over these HTMs showed that several HTMs were highly heritable, particularly those corresponding to MCARI2 as observed from nadir, and those corresponding to the first period. Potentials of such semi-automatic measurement system are discussed for in field phenotyping applications.

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