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Automated Plant Phenotyping Using 3D Machine Vision and Robotics
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Automated Plant Phenotyping Using 3D Machine Vision and Robotics. |
| 개인저자 | Bao, Yin. |
| 단체저자명 | Iowa State University. Agricultural and Biosystems Engineering. |
| 발행사항 | [S.l.]: Iowa State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 152 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438072367 |
| 학위논문주기 | Thesis (Ph.D.)--Iowa State University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Lie Tang. |
| 요약 | With the rapid advancements in genotyping technologies, plant phenotyping has become a bottleneck in exploiting the massive genomic data for crop improvement. The common practice of plant phenotyping relies on human efforts, which is labor-inten |
| 요약 | Sorghum and maize are important economic crops for food, feed, fuel, and fiber production. Manipulation of plant architecture plays a vital role in yield improvement via plant breeding. A high-throughput, field-based robotic phenotyping system w |
| 요약 | Additionally, Time-of-Flight 3D imaging was used to collect side-view point clouds of maize plants under field conditions. Algorithms for extracting plant height, leaf angle, plant orientation, and stem diameter at plant level were developed. A |
| 요약 | Various instrumentation devices for plant physiology study require accurate placement of their sensor probes toward the leaf surface. A robotic leaf probing system was developed for a controlled environment using a Time-of-Flight sensor, a laser |
| 일반주제명 | Agricultural engineering. Artificial intelligence. Robotics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
