Development and evaluation of a self-propelled electric platform for high-throughput field phenotyping in wheat breeding trials

Abstract The use of high-throughput phenotyping systems in crop research offers a powerful alternative to traditional methods for understanding plant behaviours. These systems provide a rapid, consistent, repeatable, non-destructive and objective sampling method to quantify complex and previously unobtainable traits at relatively fine resolutions. In this study, a field-based high-throughput phenotyping solution for wheat was developed using a sensor suite mounted on a self-propelled electric platform. A 2D LiDAR was used to scan wheat plots from overhead, while an odometry system was used as a local navigation system to determine the precise plot/plant/scan location. Accurate 3D models of the scanned wheat plots were reconstructed based on the recorded LiDAR and odometry data. Seven plots of different wheat cultivars were scanned to calculate the canopy height using LiDAR data, and these results were compared with manual ground truth measurements. Additionally, in each of these seven plots, the NDVI and PRI spectral indices were calculated using low-cost spectral reflectance sensors (SRSs) and an expensive visible/near-infrared (VIS/NIR) spectral analysis system used for reference purposes. The results of the validation showed good agreement between the LiDAR and manual wheat plant height measurements with an R2 of 0.73 and RMSE = 2.63 cm for three days of campaign measurements. A statistically significant linear correlation was observed between the NDVI values obtained with the reference spectrometer and the low-cost SRS; the coefficients of determination were R2 = 0.69 for day 1 and R2 = 0.81 for day 2, suggesting a similar degree of accuracy among both sensing systems. The developed platform and the obtained wheat phenotyping results demonstrated the suitability of the system for acquiring reliable data under field conditions while maintaining a constant low speed and stability during field deployment. The adaptability of the platform to the structure of the crop and the repeatability of data collection throughout the growing season make the system suitable for integration into commercial breeding programmes.