Choudhuri, Anwesa | Search NASA

DOE OSTI2023

Apparatus and method for agricultural data collection and agricultural operations

Aspects of the subject disclosure may include, for example, obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the camera is attached to a mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from encoder(s), wherein the encoder(s) are attached to the robot; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem. Additional embodiments are disclosed.

Chowdhary, Girish↗

DOE OSTI2018

Crop Stem Width Estimation in Highly Cluttered Field Environment

We present two methods for estimation of crop stem width on small mobile robots. Stem width is an important phenotype needed by breeders and plant-biologists to measure plant growth, however, its manual measurement is cumbersome, in-accurate, and inefficient. The presented methods use a common image processing core that is designed to extract the foreground in the presence of significant leaf and stem clutter, view of other rows, and varying lighting, from a side-facing USB camera mounted on a small mobile robot. Using the extracted foreground, one approach uses estimates of robot velocity from wheel encoders and dense optical flow to estimate depth, while the other employs filtering of the LIDAR 2-D point cloud to estimate the depth. Both methods have been exhaustively validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that both methods are also applicable to other crops with cylindrical stems without significant modifications. The width estimation match on sorghum is 92.5% (using vision) and 98.2% (using vision and LIDAR) when compared against manual measurements by trained agronomists. Thus, our results clearly establish the feasibility of using small robots for stem-width estimation in realistic field settings. Furthermore, the techniques presented here can be utilized for automating other phenotypic measurements.

Choudhuri, Anwesa↗