Application of Machine Learning and Hyperspectral Imaging in Plant Phenomics Research
| dc.contributor.author | Dhakal, Kshitiz | en |
| dc.contributor.committeechair | Li, Song | en |
| dc.contributor.committeemember | Zhao, Bingyu | en |
| dc.contributor.committeemember | Zhang, Bo | en |
| dc.contributor.committeemember | Oakes, Joseph Carroll | en |
| dc.contributor.committeemember | Morota, Gota | en |
| dc.contributor.department | Crop and Soil Environmental Sciences | en |
| dc.date.accessioned | 2023-03-09T09:00:08Z | en |
| dc.date.available | 2023-03-09T09:00:08Z | en |
| dc.date.issued | 2023-03-08 | en |
| dc.description.abstractgeneral | The digital imaging technology, geographical analyses tool, and computer vision (a technique that enables computers and systems to get meaningful information from images) methods can be used to extract traits-related branching pattern, canopy cover, and pod location in edamame for many plant populations in short time using less labor and resources. Using genome-wide association study, we identified several genetic markers that were associated with those traits. These markers can be used in marker-assisted selection to develop the edamame varieties that are more adaptable to mechanical harvesting and give more yield, along with understanding the physiological mechanisms for better shoot architecture traits and better yield. We used spectral signatures of different edamame at several harvesting time along with machine learning methods to identify the optimal harvest time of edamame. Hyperspectral imaging (a technique that analyzes a wide spectrum of light instead of just assigning primary colors (red, green, blue) to each pixel) when combined with computer vision and machine learning methods can be used to quantify the levels of vomitoxin (chemical that causes vomiting and feed refusal in animal and humans) for larger wheat kernel samples in a cheaper and faster way. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:36542 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/114063 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Digital imaging | en |
| dc.subject | Computer vision | en |
| dc.subject | Machine learning | en |
| dc.subject | Canopy cover | en |
| dc.subject | Mechanical harvesting | en |
| dc.subject | Deoxynivalenol | en |
| dc.subject | Hyperspectral imaging | en |
| dc.title | Application of Machine Learning and Hyperspectral Imaging in Plant Phenomics Research | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Crop and Soil Environmental Sciences | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |