國立虎尾科技大學 |

Automated Plant Phenotyping Using 3D Machine Vision and Robotics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Automated Plant Phenotyping Using 3D Machine Vision and Robotics./
作者:	Bao, Yin.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Agricultural engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780438072367

Automated Plant Phenotyping Using 3D Machine Vision and Robotics.
Bao, Yin.

Automated Plant Phenotyping Using 3D Machine Vision and Robotics. - 1 online resource (152 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Iowa State University, 2018.

Includes bibliographical references

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-intensive, time-consuming, and prone to human errors. This dissertation documents our innovative research in automated plant phenotyping using 3D machine vision and robotics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438072367Subjects--Topical Terms:

1148660
Agricultural engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Automated Plant Phenotyping Using 3D Machine Vision and Robotics.
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245 1 0 $a Automated Plant Phenotyping Using 3D Machine Vision and Robotics.
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300 $a 1 online resource (152 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Lie Tang.
502 $a Thesis (Ph.D.)--Iowa State University, 2018.
504 $a Includes bibliographical references
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-intensive, time-consuming, and prone to human errors. This dissertation documents our innovative research in automated plant phenotyping using 3D machine vision and robotics.
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 was developed to characterize plant architecture for tall-growing sorghum plants having dense population and canopies. Side-viewing stereo cameras were used for 3D reconstruction of plants. A novel data processing pipeline was developed to measure plant height, width, convex hull volume, surface area, and stem diameter. These image-derived features were highly correlated with the in-field manual measurements, and with high repeatability.
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 customized skeletonization algorithm was developed to effectively reduce a large point cloud to a skeleton graph; and a 3D Hough line detection algorithm was implemented to find individual stems. The image-derived traits showed satisfactory accuracies, except for stem diameter due to the limitations of the sensor's depth sensing precision.
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 profilometer, and a six-DOF robotic manipulator. The Time-of-Flight sensor and the laser profilometer were utilized for environment mapping and high-precision scanning of plant canopies, respectively. The environment point cloud was used for collision-free motion planning and individual plant segmentation, while the high-resolution canopy point cloud was analyzed for leaf segmentation and probing point extraction. The system achieved an average motion planning time of 0.4 s with an average probe positioning error of 1.5 mm and probe orientation error of 0.84 degrees.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Agricultural engineering. $3 1148660
650 4 $a Artificial intelligence. $3 559380
650 4 $a Robotics. $3 561941
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710 2 $a Iowa State University. $b Agricultural and Biosystems Engineering. $3 1148659
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10751453 $z click for full text (PQDT)