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Imaging Systems and Data Analysis for Plant Phenotyping
High-throughput phenotyping is becoming a critical method for many plant science researchers. Compared to manual phenotyping, high-throughput phenotyping shows advantages in the efficiency and the capability of large-scale measurements. One of the most popular existing solutions for high-throughput phenotyping is RGB image-based methods for easy access. However, traditional RGB image-based methods suffer from inaccuracy in two aspects. First, the image-based methods will inevitably lose the depth information as images are projections of 3D objects onto 2D planes. Second, the RGB image-based methods only involve three bands (i.e., Red, Green, and Blue), and information contained in other bands is lost in image capturing. To solve the bottleneck of RGB image-based methods, I have developed end-to-end solutions to tackle large-scale 3D-based data and hyperspectral data. First, I design multiple imaging systems for plant phenotyping and an end-to-end pipeline to generate the denoised point clouds. By comparing the ground truth with the extracted results, I provide insights into the performance and present evidence for increased accuracy from the imaging systems. Moreover, I present a systematic analysis of how different settings of the imaging systems affect the results. Second, using the point clouds generated by our imaging systems and pipeline, I design a method to detect the critical events on both whole plants and specific components. I implement an experiment on maize for evaluation and successfully detect events in the process of plant growth. Third, I develop a novel end-to-end platform to provide hyperspectral information for seeds, including a high-throughput imaging system and open-source software. To fully demonstrate the platform's effectiveness, I conduct a classification of seeds using machine learning models. Moreover, my experiment has shown the potential for seed segmentation with different seed species.
Gao, Tian, "Imaging Systems and Data Analysis for Plant Phenotyping" (2022). ETD collection for University of Nebraska-Lincoln. AAI29167763.