The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on January 22, 2010). Thesis advisor: Dr. Reid J. Smeda. Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 130-144). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 130-144). Also available on the Internet.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1967. / Typescript. Vita. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1965. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

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.