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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

Multispectral data from two legume-grass mixtures (clover- and lucerne-grass) were collected in the year 2018 for aboveground biomass and nitrogen fixation (NFix) estimation. In addition to the mixtures, pure stands of legumes and of grasses of the two mixtures were sown in order to represent variable conditions in practical farming (0-100% legumes). All six treatments were cultivated in four replicates and harvested three times within the year (plot size: 1.5 x 12 m). Destructive biomass samples for fresh (FM) and dry matter (DM) and NFix determination were taken three times at harvest. To cover the entire vegetation season, sub-sampling for DM and FM was done five times between the harvests. Flight missions were carried out one day before each of the eight sampling dates. A multispectral sensor (Parrot Sequoia, MicaSense Inc, Seattle, USA) with four spectral bands (green, red, red edge, near infrared) was mounted on a low-cost unmanned aerial vehicle (UAV; DJI Phantom 3, Advanced, Shenzhen, China). Eight black and white ground control points (GCPs) were distributed in the pathways. Coordinates of the plot corners and GCPs were measured by a Leica real time kinematic global navigation satellite system (Leica RTK GNSS). Orthomosaics were created by the overlapping images with a photogrammetric processing software (Agisoft PhotoScan Professional, Agisoft LLC, St. Petersburg, Russia). The orthomosaics were georeferenced using the coordinates of the GCPs. The mean reflectance value of the four bands was extracted by zonal statistics in QGIS (Quantum Geografic Infromation System) using the four plot corners of each plot as boundaries. Furthermore, eight texture features of every band were calculated, provided by the processing tool HaralickTextureExtraction of the Orfeo Toolbox library (OTB) in QGIS.

Gefördert durch den Publikationsfonds der Universität Kassel