High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”