  RGB-D cameras have been successfully used for indoor High-ThroughpuT
Phenotyping (HTTP). However, their capability and feasibility for in-field HTTP
still need to be evaluated, due to the noise and disturbances generated by
unstable illumination, specular reflection, and diffuse reflection, etc. To
solve these problems, we evaluated the depth-ranging performances of two
consumer-level RGB-D cameras (RealSense D435i and Kinect V2) under in-field
HTTP scenarios, and proposed a strategy to compensate the depth measurement
error. For performance evaluation, we focused on determining their optimal
ranging areas for different crop organs. Based on the evaluation results, we
proposed a brightness-and-distance-based Support Vector Regression Strategy, to
compensate the ranging error. Furthermore, we analyzed the depth filling rate
of two RGB-D cameras under different lighting intensities. Experimental results
showed that: 1) For RealSense D435i, its effective ranging area is [0.160,
1.400] m, and in-field filling rate is approximately 90%. 2) For Kinect V2, it
has a high ranging accuracy in the [0.497, 1.200] m, but its in-field filling
rate is less than 24.9%. 3) Our error compensation model can effectively reduce
the influences of lighting intensity and target distance. The maximum MSE and
minimum R2 of this model are 0.029 and 0.867, respectively. To sum up,
RealSense D435i has better ranging performances than Kinect V2 on in-field
HTTP.
