Literature DB >> 16968884

Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

M Möller1, V Alchanatis, Y Cohen, M Meron, J Tsipris, A Naor, V Ostrovsky, M Sprintsin, S Cohen.   

Abstract

Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.

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Year:  2006        PMID: 16968884     DOI: 10.1093/jxb/erl115

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  20 in total

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2.  Phenotyping maize for adaptation to drought.

Authors:  Jose L Araus; María D Serret; Gregory O Edmeades
Journal:  Front Physiol       Date:  2012-08-10       Impact factor: 4.566

3.  An automated field phenotyping pipeline for application in grapevine research.

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Journal:  Sensors (Basel)       Date:  2015-02-26       Impact factor: 3.576

Review 4.  A review of imaging techniques for plant phenotyping.

Authors:  Lei Li; Qin Zhang; Danfeng Huang
Journal:  Sensors (Basel)       Date:  2014-10-24       Impact factor: 3.576

5.  A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.

Authors:  Maria Tattaris; Matthew P Reynolds; Scott C Chapman
Journal:  Front Plant Sci       Date:  2016-08-03       Impact factor: 5.753

6.  Diverging Drought Resistance of Scots Pine Provenances Revealed by Infrared Thermography.

Authors:  Hannes Seidel; Christian Schunk; Michael Matiu; Annette Menzel
Journal:  Front Plant Sci       Date:  2016-08-31       Impact factor: 5.753

7.  The heterogeneity and spatial patterning of structure and physiology across the leaf surface in giant leaves of Alocasia macrorrhiza.

Authors:  Shuai Li; Yong-Jiang Zhang; Lawren Sack; Christine Scoffoni; Atsushi Ishida; Ya-Jun Chen; Kun-Fang Cao
Journal:  PLoS One       Date:  2013-06-11       Impact factor: 3.240

8.  Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints.

Authors:  Nicolas Virlet; Valentine Lebourgeois; Sébastien Martinez; Evelyne Costes; Sylvain Labbé; Jean-Luc Regnard
Journal:  J Exp Bot       Date:  2014-07-30       Impact factor: 6.992

9.  Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery.

Authors:  Shan-e-Ahmed Raza; Hazel K Smith; Graham J J Clarkson; Gail Taylor; Andrew J Thompson; John Clarkson; Nasir M Rajpoot
Journal:  PLoS One       Date:  2014-06-03       Impact factor: 3.240

10.  Vineyard water status assessment using on-the-go thermal imaging and machine learning.

Authors:  Salvador Gutiérrez; María P Diago; Juan Fernández-Novales; Javier Tardaguila
Journal:  PLoS One       Date:  2018-02-01       Impact factor: 3.240

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