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Development of a site-specific herbi...

Givens, Wade Alexander.

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Development of a site-specific herbicide application decision support system.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Development of a site-specific herbicide application decision support system./
Author:	Givens, Wade Alexander.
Description:	95 p.
Notes:	Adviser: David R. Shaw.
Contained By:	Masters Abstracts International45-04.
Subject:	Agriculture, Agronomy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441766

Development of a site-specific herbicide application decision support system.
Givens, Wade Alexander.

Development of a site-specific herbicide application decision support system. - 95 p.

Adviser: David R. Shaw.

Thesis (M.S.)--Mississippi State University, 2007.

Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. Neither the pattern nor method impacted the results of the predicted average values for a given weed species.Subjects--Topical Terms:

1018679
Agriculture, Agronomy.

Development of a site-specific herbicide application decision support system.
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035 $a (UMI)AAI1441766
035 $a AAI1441766
040 $a UMI $c UMI
100 1 $a Givens, Wade Alexander. $3 1272593
245 1 0 $a Development of a site-specific herbicide application decision support system.
300 $a 95 p.
500 $a Adviser: David R. Shaw.
500 $a Source: Masters Abstracts International, Volume: 45-04, page: 1841.
502 $a Thesis (M.S.)--Mississippi State University, 2007.
520 $a Sampling techniques and interpolation methods were compared to assess the accuracy of each pattern/method combination. Neither the pattern nor method impacted the results of the predicted average values for a given weed species.
520 $a Weeds typically grow in patches across agricultural landscapes. Because of this characteristic growth pattern, it seems logical to apply herbicides site-specifically to control them. The primary objective of this research was to develop a site-specific herbicide decision support system. Additional objectives include evaluating the effects of sampling patterns and interpolation techniques for weed mapping accuracy and evaluating texture analysis for weed patch detection in row-crops.
520 $a The last objective addressed was texture analysis' ability to distinguish weed patches in row-crops. Texture analysis performed better on late season imagery, with the highest classification accuracies (45 to 75%) coming from distinguishing areas that were below a given weed threshold from those that were above.
590 $a School code: 0132.
650 4 $a Agriculture, Agronomy. $3 1018679
650 4 $a Agriculture, Plant Pathology. $3 1028950
650 4 $a Agriculture, Soil Science. $3 1017824
690 $a 0285
690 $a 0480
690 $a 0481
710 2 $a Mississippi State University. $3 1017550
773 0 $t Masters Abstracts International $g 45-04.
790 $a 0132
790 1 0 $a Shaw, David R., $e advisor
791 $a M.S.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441766