Activity
Time:
Ph.D. Dissertation Defense: Tian Gao
Date:
2:30 pm –
3:30 pm
Zoom Room: https://unl.zoom.us/j/91473308340
“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.
Committee members:
Hongfeng Yu (Chair of Committee)
Juan Cui (Reader)
Lisong Xu (Reader)
Harkamal Walia (Outside Representative)
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.
Committee members:
Hongfeng Yu (Chair of Committee)
Juan Cui (Reader)
Lisong Xu (Reader)
Harkamal Walia (Outside Representative)