東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A combined statistical-soft computin...

Camargo Neto, Joao.

FindBook

Google Book

Amazon

博客來

A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems./
作者:	Camargo Neto, Joao.
面頁冊數:	170 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4691.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Engineering, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3147135
ISBN:	049605595X

A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems.
Camargo Neto, Joao.

A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems. - 170 p.

Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4691.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2004.

This dissertation describes a combined statistical-soft computing approach for classifying and mapping weeds species using color images in minimum-tillage systems. A new unsupervised separation index (ExGExR) is introduced to distinguish plant canopies from different soil/residue backgrounds. Results showed that ExGExR was significantly improved for all species and all three weeks over the previously published excess green (ExG). ExGExR performed very well for separating both pigweed and velvetleaf from bare soil and corn stalk backgrounds during the first and second week after crop emergence.

ISBN: 049605595XSubjects--Topical Terms:

1019504
Engineering, Agricultural.

A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems.
LDR:03196nmm 2200313 4500 001 1845166
005 20051012083926.5
008 130614s2004 eng d
020 $a 049605595X
035 $a (UnM)AAI3147135
035 $a AAI3147135
040 $a UnM $c UnM
100 1 $a Camargo Neto, Joao. $3 1933331
245 1 2 $a A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems.
300 $a 170 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4691.
500 $a Supervisor: George E. Meyer.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2004.
520 $a This dissertation describes a combined statistical-soft computing approach for classifying and mapping weeds species using color images in minimum-tillage systems. A new unsupervised separation index (ExGExR) is introduced to distinguish plant canopies from different soil/residue backgrounds. Results showed that ExGExR was significantly improved for all species and all three weeks over the previously published excess green (ExG). ExGExR performed very well for separating both pigweed and velvetleaf from bare soil and corn stalk backgrounds during the first and second week after crop emergence.
520 $a A new algorithm for individual leaf extraction was introduced based on fuzzy color clustering and genetic algorithm. Images of green canopies were segmented into fragments of potential leaf regions using clustering algorithm. Fragments were then reassembled into individual leaves using genetic optimization algorithm. The algorithm performance was evaluated by comparing the actual number leaves automatically extracted with the number of potential leaves observed visually. An overall performance of 75% for leaves correctly extracted was obtained.
520 $a Elliptic Fourier method was next tested for characterizing the shape of hand selected young soybean, sunflower, red root pigweed, and velvetleaf leaves. Discriminant analysis of these shape coefficients suggested that the third week after emergence was the best time to identify plant species with a correct classification average of 89.4%. When leaves from the second and third week were analyzed a correct classification average of 89.2% was reached.
520 $a An unsupervised method for plant species identification was finally tested. Elliptic Fourier descriptors not only provided leaflet shape information, but also a lamina boundary template, controlling where textural features were computed. Each lamina shape extracted was corrected such that all leaflets had the same orientation for texture extraction. SAS PROC DISCRIM procedure was performed to build a species classification model using selected Fourier coefficients and local homogeneity and entropy texture features. An overall success rate of 86% was obtained for plant species classification.
590 $a School code: 0138.
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Agriculture, Agronomy. $3 1018679
690 $a 0539
690 $a 0285
710 2 0 $a The University of Nebraska - Lincoln. $3 1024939
773 0 $t Dissertation Abstracts International $g 65-09B.
790 1 0 $a Meyer, George E., $e advisor
790 $a 0138
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3147135