Abstract
Abstract. Multitemporal drone surveys are a perfect tool to determine various geometric and spectral crop parameters for rapid phenotyping in field trials. Depending on the geometric resolution and the size of the crop, information at leaf level or canopy level can be obtained. The focus of this paper is to demonstrate which geometric properties can be automatically derived from high resolution drone imagery during the vegetation period. For this research approx. 1920 cauliflower with a large genetic variety were planted and monitored by five different drone surveys at an altitude of 20 m, using a high resolution 36 Mpix. RGB-camera. In order to minimize intensive radiometric calibration, BRDF effects and eliminate shade, flights were carried out at overcast skies. After photogrammetric image processing, detailed crop height models (CHM) were computed. 10 distinct crop parameters were derived from a combination of the orthophotos, the CHM and additional information. According to the phenological phase a specific set of parameters was developed for every flight. For instance, the position of the individual plants is computed right after the first flight. For the flight prior to harvesting, an algorithm for the head diameter and the curvature of the cauliflower heads was developed. Geometric parameters are generally better suited for automation, because they require less specific ground truth or reference information, than spectrally derived biophysical parameters.
Highlights
Unmanned aerial systems (UAS) are in many ways a a very convenient tool for precision farming applications in general and field trials in particular, as many plant-relevant information can be collected at defined times, quickly, without contact, objectively and automatically, e.g. (Hunt & Daughtry, 2018), (Maes & Steppe, 2018)
Most of the published drone related phenotyping research is related to common crops such as wheat, barley, corn, sorghum etc
Spectral and temporal properties may derived from the plants, figure 1. For efficient phenotyping this assumes that the data processing of the drone data and data analysis for the evaluation by the agronomist is more or less automatic
Summary
Unmanned aerial systems (UAS) are in many ways a a very convenient tool for precision farming applications in general and field trials in particular, as many plant-relevant information can be collected at defined times, quickly, without contact, objectively and automatically, e.g. (Hunt & Daughtry, 2018), (Maes & Steppe , 2018). Various problems are associated with the scale transitions This applies in particular to the fact that the spectral reflection properties change greatly depending on whether they are measured on a single leaf (leaf level) or represent an average value of several components, such as soil, plant and shade (canopy level). A lot of information can be derived from the UAS images, where the geometric, spectral or temporal properties of the plant development are in the foreground Geometric parameters, such as the height of growth or the degree of soil cover, can be calculated automatically as far as possible with inexpensive sensors (color digital camera) and will be presented in more detail in the following. The surveys were carried out systematically with a longitudinal and transverse overlap of 80% and 60%, respectively
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