ASABE Technical Library - Abstract

Abstract. Phenomics research, which involves the determination of the relationship between phenotypes and genotypes, is constrained by the difficulties in phenotyping. Typically, the traits or characteristics of plants are manually measured or visually evaluated by humans for purposes such as, breeding and crop science. Therefore, general phenotyping is laborious, expensive, and subjective, whereas genotyping is readily conducted via DNA-sequencing techniques. The objective of this study was to develop a high-throughput phenotyping system that used field imagery from an unmanned aerial vehicle (UAV). The UAV was a multi-rotor drone that flew along a path preset by a global positioning system (GPS)-based navigation system. During the flight, a camera mounted on the UAV captured images of the field every 2 s. Because diseases on the leaves could be detected by image processing, disease severity was assessed via the aerial images of a potato field for breeding purposes. The areas under disease progress curves (AUDPCs) from the aerial images and conventional visual assessment were found to have a correlation; the coefficient of determination was R² = 0.77. In the large test field for crop studies, the aerial images were assembled, and a composite image of the entire field was generated. Furthermore, a three-dimensional (3D) model of the field was generated as a point cloud with 3D reconstruction techniques called structure from motion and semi-global dense matching. With these techniques, the physical size of the plants in the field, which is one of the growth parameters, was retrievable as digital information.