Agriculture-related industry and academia have widely adopted Hyperspectral Imaging
(HSI) based in-field phenotyping activities. Current HSI solutions such as airborne remote sensing
platforms and handheld spectrometers have been proven effective and have become popular in
various phenotyping applications. However, the quality of remote sensing systems suffers from a
low signal-over-noise ratio due to the imaging distance and low resolution. Handheld leaf
spectrometers are slow, labor-intensive, and only measure a small spot on the leaf, which fails to
represent the canopy variation. In 2018, the Purdue ABE sensor lab developed a new handheld
hyperspectral leaf imager, LeafSpec. For the first time, field phenotyping researchers were able to
collect high-resolution leaf hyperspectral images without the negative impacts of ambient lighting
and leaf-slope angle changes. LeafSpec has been successfully tested in field assays and showed its
advantageous phenotyping quality. The goal of this study was to test the hypothesis that a robotic
system could replace the human operator required to perform in-field and leaf-level HSI using
LeafSpec. The system consisted of a modified version of the LeafSpec device, a machine vision
system for target leaf detection, a National Instrument MyRIO as a controller and a customized
cartesian robotic arm with five Degrees of Freedom (DOF). For each scan, the on-board machine
vision system recognized the top leaf collar and obtained the target coordinates. The coordinates
were then passed to the controller, which calculated the appropriate path and acceleration profile
and drove the arm to approach the target leaf and scan the leaf with the LeafSpec device. The
scanned image was then processed in real-time to calculate plant physiological features such as
chlorophyll content, nitrogen content and so on. In the 2019 field test, the designed system
collected data from 41 corn plants with two genotypes and three levels of nitrogen treatments with
an average cycle time of 86 seconds. The nitrogen content predicted by the designed system had
an R squared of 0.72 with the ground truth. The developers, therefore, concluded that the robotic
gantry system was capable of replacing human operators for LeafSpec hyperspectral corn leaf
imaging in the field with high quality.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12264881 |
| Date | 21 June 2022 |
| Creators | Ziling Chen (8810570) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Automated_In-Field_Leaf-Level_Hyperspectral_Imaging_of_Corn_Plants_Using_a_Cartesian_Robotic_Platform/12264881 |
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