Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements

In agriculture, information about the spatial distribution of crop height is valuable for applications such as biomass and yield estimation, or increasing field work efficiency in terms of fertilizing, applying pesticides, irrigation, etc. Established methods for capturing crop height often comprise restrictions in terms of cost and time efficiency, flexibility, and temporal and spatial resolution of measurements. Furthermore, crop height is mostly derived from a measurement of the bare terrain prior to plant growth and measurements of the crop surface when plants are growing, resulting in the need of multiple field campaigns. In our study, we examine a method to derive crop heights directly from data of a plot of full grown maize plants captured in a single field campaign. We assess continuous raster crop height models (CHMs) and individual plant heights derived from data collected with the low-cost 3D camera Microsoft^® Kinect^® for Xbox One™ based on a comprehensive comparison to terrestrial laser scanning (TLS) reference data.

We examine single measurements captured with the 3D camera and a combination of the single measurements, i.e. a combination of multiple perspectives. The quality of both CHMs, and individual plant heights is improved by combining the measurements. R² of CHMs derived from single measurements range from 0.48 to 0.88, combining all measurements leads to an R² of 0.89. In case of individual plant heights, an R² of 0.98 is achieved for the combined measures (with R² = 0.44 for the single measurements). The crop heights derived from the 3D camera measurements comprise an average underestimation of 0.06 m compared to TLS reference values.

We recommend the combination of multiple low-cost 3D camera measurements, removal of measurement artefacts, and the inclusion of correction functions to improve the quality of crop height measurements. Operating low-cost 3D cameras under field conditions on agricultural machines or on autonomous platforms can offer time and cost efficient tools for capturing the spatial distribution of crop heights directly in the field and subsequently to advance agricultural efficiency and productivity. More general, all processes which include the 3D geometry of natural objects can profit from low-cost methods producing 3D geodata.