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

Machine vision systems for real-time...

Tang, Lie.

Linked to FindBook

Google Book

Amazon

博客來

Machine vision systems for real-time plant variability sensing and in-field application.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine vision systems for real-time plant variability sensing and in-field application./
Author:	Tang, Lie.
Description:	150 p.
Notes:	Adviser: Lei Tian.
Contained By:	Dissertation Abstracts International63-02B.
Subject:	Agriculture, Agronomy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3044237
ISBN:	049358112X

Machine vision systems for real-time plant variability sensing and in-field application.
Tang, Lie.

Machine vision systems for real-time plant variability sensing and in-field application. - 150 p.

Adviser: Lei Tian.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.

The goal of this research was to advance precision agriculture research by promoting machine vision technologies for real-time plant variability sensing and in-field application. Specifically, research efforts were focused on the development and implementation of engineering solutions for crop and weed sensing, variable-rate control, and vehicle positioning, the three fundamental components required for a machine vision system to function by the principles of precision agriculture.

ISBN: 049358112XSubjects--Topical Terms:

1018679
Agriculture, Agronomy.

Machine vision systems for real-time plant variability sensing and in-field application.
LDR:03625nam 2200361 a 45 001 932310
005 20110505
008 110505s2002 eng d
020 $a 049358112X
035 $a (UnM)AAI3044237
035 $a AAI3044237
040 $a UnM $c UnM
100 1 $a Tang, Lie. $3 1256059
245 1 0 $a Machine vision systems for real-time plant variability sensing and in-field application.
300 $a 150 p.
500 $a Adviser: Lei Tian.
500 $a Source: Dissertation Abstracts International, Volume: 63-02, Section: B, page: 0894.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.
520 $a The goal of this research was to advance precision agriculture research by promoting machine vision technologies for real-time plant variability sensing and in-field application. Specifically, research efforts were focused on the development and implementation of engineering solutions for crop and weed sensing, variable-rate control, and vehicle positioning, the three fundamental components required for a machine vision system to function by the principles of precision agriculture.
520 $a A supervised color image segmentation scheme using a binary-coded genetic algorithm (GA) was developed for vegetation detection in hue-saturation-intensity (HSI) color space under outdoor lighting conditions. The results showed that this innovative segmentation scheme performed equally well as compared to other cluster analysis-based segmentation schemes.
520 $a A novel texture-based broadleaf and grass classification method was developed using a low-level Gabor wavelet filter bank-based feature extraction algorithm and a high-level neural network-based pattern recognition algorithm. A 100% classification accuracy was achieved when classifying the test samples from five weed species under real-time constraints.
520 $a To promote the adoption of the machine vision-based selective spraying technology, the sensing system cost was significantly reduced through the incorporation of the low-cost video-conferencing cameras. A prototype system was built and validated through experimentation.
520 $a To improve the accuracy of sprayer position and orientation estimation, a Kalman filtering sensor fusion technique was implemented to integrate the GPS system and wheel encoders. The developed positioning system reduced the position error by 80% as shown by evaluative tests, where machine vision was innovatively introduced to generate sub-centimeter accuracy validation tracks.
520 $a Through the addition of new hardware and the enhancement of the software functionality, the developed machine vision-based selective spraying system was further converted into a multifunctional platform for both real-time in-field variability mapping and selectively spraying.
520 $a In-field variations associated with corn plant spacing, growth stage, and population can lead to a significant yield differences. Since the ability to reduce these variations is directly related to the planter performance, a machine vision-based emerged corn plant sensing system (ECS) was developed for the performance evaluation for prototype planters. With the real-time image sequencing capability, the system also achieved an average spacing measurement error of less than 10 mm.
590 $a School code: 0090.
650 4 $a Agriculture, Agronomy. $3 1018679
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0285
690 $a 0539
690 $a 0544
710 2 0 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 63-02B.
790 $a 0090
790 1 0 $a Tian, Lei, $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3044237