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020 ▼a 9780438072367
035 ▼a (MiAaPQ)AAI10751453
035 ▼a (MiAaPQ)iastate:17154
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 631
1001 ▼a Bao, Yin. ▼0 (orcid)0000-0002-3548-1823
24510 ▼a Automated Plant Phenotyping Using 3D Machine Vision and Robotics.
260 ▼a [S.l.] : ▼b Iowa State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 152 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Lie Tang.
5021 ▼a Thesis (Ph.D.)--Iowa State University, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0097.
650 4 ▼a Agricultural engineering.
650 4 ▼a Artificial intelligence.
650 4 ▼a Robotics.
690 ▼a 0539
690 ▼a 0800
690 ▼a 0771
71020 ▼a Iowa State University. ▼b Agricultural and Biosystems Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0097
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997173 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자