東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep Learning for Field-based Automa...

Zhang, Zhisheng.

Linked to FindBook

Google Book

Amazon

博客來

Deep Learning for Field-based Automated High-throughput Plant Phenotyping.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Field-based Automated High-throughput Plant Phenotyping./
Author:	Zhang, Zhisheng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 81-05.
Contained By:	Masters Abstracts International81-05.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22588541
ISBN:	9781088369135

Deep Learning for Field-based Automated High-throughput Plant Phenotyping.
Zhang, Zhisheng.

Deep Learning for Field-based Automated High-throughput Plant Phenotyping. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 47 p.

Source: Masters Abstracts International, Volume: 81-05.

Thesis (M.S.)--Iowa State University, 2019.

This item must not be sold to any third party vendors.

The current rapid development trend in Artificial Intelligence (AI) provides a vast selection of high-quality tools to solve complex problems in more efficient ways than before. As a consequence, many fields of science and engineering are starting to explore AI tools, especially Deep Learning (DL) models for visual perception, audio understanding and decision making.This thesis explores the application of DL in plant science and agriculture to overcome the throughput bottleneck inherent in the currently practiced manual phenotyping in fields. Among many field-based phenotyping challenges, we focus on the problems of pod-counting and flower detection in soybean crop production. To accomplish this objective, we leverage the RetinaNet DL model with different backbones to process the raw image data collected by autonomous ground-robotic platforms. The proposed high-throughput phenotyping framework also involves tracking algorithms for robust decision-making using multiple image frames from the video data collected by the robots. In the thesis, we discuss the training data generation, model building and inference processes in detail. High degree of accuracy results presented in this study demonstrate the promise of DL tools for field-based automated high-throughput plant phenotyping. Hence, a framework such as the one presented here can dramatically transform agriculture in terms of scalability, precision and profitability.

ISBN: 9781088369135Subjects--Topical Terms:

586835
Engineering.

Deep Learning for Field-based Automated High-throughput Plant Phenotyping.
LDR:02408nmm a2200313 4500 001 2264322
005 20200423113000.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781088369135
035 $a (MiAaPQ)AAI22588541
035 $a AAI22588541
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Zhisheng. $3 3541440
245 1 0 $a Deep Learning for Field-based Automated High-throughput Plant Phenotyping.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 81-05.
500 $a Advisor: Sarkar, Soumik.
502 $a Thesis (M.S.)--Iowa State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a The current rapid development trend in Artificial Intelligence (AI) provides a vast selection of high-quality tools to solve complex problems in more efficient ways than before. As a consequence, many fields of science and engineering are starting to explore AI tools, especially Deep Learning (DL) models for visual perception, audio understanding and decision making.This thesis explores the application of DL in plant science and agriculture to overcome the throughput bottleneck inherent in the currently practiced manual phenotyping in fields. Among many field-based phenotyping challenges, we focus on the problems of pod-counting and flower detection in soybean crop production. To accomplish this objective, we leverage the RetinaNet DL model with different backbones to process the raw image data collected by autonomous ground-robotic platforms. The proposed high-throughput phenotyping framework also involves tracking algorithms for robust decision-making using multiple image frames from the video data collected by the robots. In the thesis, we discuss the training data generation, model building and inference processes in detail. High degree of accuracy results presented in this study demonstrate the promise of DL tools for field-based automated high-throughput plant phenotyping. Hence, a framework such as the one presented here can dramatically transform agriculture in terms of scalability, precision and profitability.
590 $a School code: 0097.
650 4 $a Engineering. $3 586835
650 4 $a Plant sciences. $3 3173832
650 4 $a Computer science. $3 523869
690 $a 0537
690 $a 0479
690 $a 0984
710 2 $a Iowa State University. $b Mechanical Engineering. $3 1023689
773 0 $t Masters Abstracts International $g 81-05.
790 $a 0097
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22588541