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Noncontact and early detection of pl...

Kacira, Murat.

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Noncontact and early detection of plant water stress using infrared thermometry and image processing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Noncontact and early detection of plant water stress using infrared thermometry and image processing./
作者:	Kacira, Murat.
面頁冊數:	256 p.
附註:	Adviser: Peter P. Ling.
Contained By:	Dissertation Abstracts International61-02B.
標題:	Agriculture, Agronomy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9962407
ISBN:	059966486X

Noncontact and early detection of plant water stress using infrared thermometry and image processing.
Kacira, Murat.

Noncontact and early detection of plant water stress using infrared thermometry and image processing. - 256 p.

Adviser: Peter P. Ling.

Thesis (Ph.D.)--The Ohio State University, 2000.

A methodology for early, non-contact, non-destructive, and quantitative detection of plant water stress for plants grown in controlled environments was developed with applications of infrared thermometry using crop water stress index (CWSI) and image processing using top projected canopy area (TPCA) of the plants.

ISBN: 059966486XSubjects--Topical Terms:

1018679
Agriculture, Agronomy.

Noncontact and early detection of plant water stress using infrared thermometry and image processing.
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100 1 $a Kacira, Murat. $3 1250237
245 1 0 $a Noncontact and early detection of plant water stress using infrared thermometry and image processing.
300 $a 256 p.
500 $a Adviser: Peter P. Ling.
500 $a Source: Dissertation Abstracts International, Volume: 61-02, Section: B, page: 0951.
502 $a Thesis (Ph.D.)--The Ohio State University, 2000.
520 $a A methodology for early, non-contact, non-destructive, and quantitative detection of plant water stress for plants grown in controlled environments was developed with applications of infrared thermometry using crop water stress index (CWSI) and image processing using top projected canopy area (TPCA) of the plants.
520 $a A computer-controlled system was designed and built for continuous monitoring of plant health and growth. A crop water stress index model for plants grown under controlled environments was developed using basic thermodynamic principles and the energy balance of the plant. Model predicted CWSI values were correlated with measured CWSI values with R<super>2</super> values of 0.83, 0.50, 0.79, and 0.76 for the experiments conducted. An inverse and linear correlation was found between crop water stress index and measured evapotranspiration rates. The leaf temperatures of the stressed plants were found to be 1–3°C higher than the air temperature. The leaf temperatures of well-watered plants were consistently lower (1–4°C) than air temperature during the experiments.
520 $a Top projected canopy area (TPCA) of the plants was extracted from plant images using machine vision and image processing techniques. TPCA expansion of the plants in the treatment group was temporarily inhibited as the plants experienced water stress. Following the irrigation, as the plants recovered from water stressed condition, the TPCA expansion continued to increase. TPCA gains of the plants in the treatment group were affected by water stress and they were less than the TPCA gains of the plants in the control group. Baselines were established with a parametric approach using CWSI and coefficient of variation of TPCA (COV of TPCA) of the plants for early detection of the water stress. The baselines using only CWSI as an indicator for early water stress detection were found to be 0.14, 0.12, 0.20, and 0.10, and were 0.40, 0.55, 0.70, and 0.36 when only COV of TPCA was used as an indicator. The effectiveness of the sensing techniques was evaluated using timing of the stress detection by human. The CWSI based technique was able to detect the stress one to two days prior to the time of stress detection by human while the detection with TPCA based approach was found to be mostly 5 hours prior to the stress detection by human. Overall results of this study suggested that early and non-contact detection of plant water stress using CWSI was more successful and was quicker compared to the TPCA based water stress detection.
590 $a School code: 0168.
650 4 $a Agriculture, Agronomy. $3 1018679
650 4 $a Engineering, Agricultural. $3 1019504
690 $a 0285
690 $a 0539
710 2 0 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 61-02B.
790 $a 0168
790 1 0 $a Ling, Peter P., $e advisor
791 $a Ph.D.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9962407