Canopy Temperature-Based Water Stress Indices: Potential and Limitations

Nanda, M. K.; Giri, Utpal; Bera, Nimai

doi:10.1007/978-981-13-1861-0_14

Cited by 19 publications

(8 citation statements)

References 47 publications

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“…Canopy temperatures were measured using the thermal camera mounted on a 5‐m aboveground tripod with a setup distance in the camera of 5 m from the plot. Previous studies identified the canopy thermal radiation variances with sensor view angle (Kimes et al, 1980; Kirkham, 2005; Krayenhoff and Voogt, 2016; Nanda et al, 2018). Consequently, the thermal camera images of the canopy were consistently taken perpendicular to the plant surface, that is, in the nadir direction.…”

Section: Methodsmentioning

confidence: 99%

An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study

2019

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Nitrogen is one of the most important yield‐limiting nutrients for corn (Zea mays). The ability of thermal remote sensing to detect nitrogen deficiency in corn may enable precision agriculture to modify nitrogen rates according to field conditions. This study applies the exergy destruction principle as a theory to explain the inverse relationship between surface temperature and nitrogen rate. Two hypotheses were developed. First, it was hypothesized that agricultural crops experiencing greater growth and providing greater yield will have lower surface temperature. The second hypothesis was that corn grown under optimum levels of nitrogen will have lower surface temperatures compared to corn grown under nitrogen stressed conditions. Field studies were conducted during two summer seasons (2016 and 2017) on an established long‐term field trial of corn yield response to varying rates of nitrogen. It was found that corn surface temperature decreased as the rate of nitrogen increased. A shallow but statistically significant (P < 0.05) negative slope was observed consistently with increasing rates of nitrogen. Surface temperature measurements, however, were variable. This variability was the result of external and weather dependent variables that influenced leaf surface temperature. Despite this variability, the exergy destruction principle provides a theory from which thermal remote sensing can be applied through the use of surface temperature measurements to detect physiological stress in crop plants. Core Ideas Thermal remote sensing was proposed to detect nitrogen stress in corn plants. Nitrogen stressed plants had higher surface temperatures than less stressed plants. Temperature trends were consistent with the exergy destruction principle. Nitrogen‐temperature correlations were statistically significant at the 0.05 significance level. Corn yield increases with nitrogen rate increase and surface temperature decrease.

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Section: Methodsmentioning

confidence: 99%

An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study

2019

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“…Additionally, microclimate and other stresses, that is, nutrient deficiency and diseases must be considered as factors restricting the CWSI's ability to predict final maize grain yield (Nanda et al, 2018). Irmak et al (2000) assessed CWSI of maize under field conditions in a Mediterranean semiarid climate and predicted maize yield using CWSI.…”

Section: Cwsi Indicates Water Stress Levels Of Maizementioning

confidence: 99%

Thermal imaging for assessment of maize water stress and yield prediction under drought conditions

Pradawet

Khongdee

Pansak

et al. 2022

J Agronomy Crop Science

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Maize production in Thailand is increasingly suffering from drought periods along the cropping season. This creates the need for rapid and accurate methods to detect crop water stress to prevent yield loss. The study was, therefore, conducted to improve the efficacy of thermal imaging for assessing maize water stress and yield prediction. The experiment was carried out under controlled and field conditions in Phitsanulok, Thailand. Five treatments were applied, including (T1) fully irrigated treatment with 100% of crop water requirement (CWR) as control; (T2) early stress with 50% of CWR from 20 days after sowing (DAS) until anthesis and subsequent rewatering; (T3) sustained deficit at 50% of CWR from 20 DAS until harvest; (T4) late stress with 100% of CWR until anthesis and 50% of CWR after anthesis until harvest; (T5) late stress with 100% of CWR until anthesis and no irrigation after anthesis. Canopy temperature (FLIR), crop growth and soil moisture were measured at 5‐day‐intervals. Under controlled conditions, early water stress significantly reduced maize growth and yield. Water deficit after anthesis had no significant effect. A new combination of wet/dry sponge type reference surfaces was used for the determination of the Crop Water Stress Index (CWSI). There was a strong relationship between CWSI and stomatal conductance (R² = 0.90), with a CWSI of 0.35 being correlated to a 64%‐yield loss. Assessing CWSI at 55 DAS, that is, at tasseling, under greenhouse conditions corresponded best to the final maize yield. This linear regression model validated well in both maize lowland (R² = 0.94) and maize upland fields (R² = 0.97) under the prevailing variety, soil and climate conditions. The results demonstrate that, using improved standardized references and data acquisition protocols, thermal imaging CWSI monitoring according to critical phenological stages enables yield prediction under drought stress.

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“…Thermography is an imaging technique of infrared radiation, which visualizes an object using a heat signature (Walter et al., 2012). The transpiration rate can be reduced due to the stomata of leaves close, under water‐deficit conditions (Nanda et al., 2018). Due to lowering transpiration rate, plant canopy temperature of well‐watered plants can be lower than plants that are water stressed (Walter et al., 2012).…”

Section: Sensors For High‐throughput Phenotypingmentioning

confidence: 99%

Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass (Lolium perenneL.)—A review

Jayasinghe

Badenhorst

Jacobs

et al. 2021

Grass and Forage Science

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Perennial ryegrass (Lolium perenne L.) is considered the most important pasture species in temperate agriculture, with over six million hectares of sown area in Australia alone. However, perennial ryegrass has poor persistence in some environments because of low tolerance to a range of both abiotic and biotic stresses. To breed perennial ryegrass, cultivars with greater persistence and productivity may require evaluation of genotypes over a number of years. Persistence assessment in pasture breeding depends on manual ground cover estimation or counting the number of surviving plants or tillers in a known area. These methods are subjective and labour intensive, which may limit data collection in large‐scale breeding programs. With the rapid development of sensors and image processing algorithms, image‐based high‐throughput phenotyping (HTP) is becoming commonplace in the breeding of major food crops. Image‐based HTP approaches consist of the deployment of a wide range of sensors on ground‐based or airborne platforms and data analysed through image processing pipelines. Image‐based HTP shows high potential for use in pasture phenotyping in breeding programs and may be able to reduce timeframes for releasing new cultivars. Moreover, existing image‐based HTP approaches could be further developed to include precise tools for phenotyping pasture persistence traits such as pasture senescence, botanical composition, pathogen and pest resistance. In this study, we reviewed existing image‐based HTP approaches in precision agriculture and discussed their feasibility for perennial ryegrass persistence estimation in pasture breeding. Although the paper focuses on application in perennial ryegrass, the principles equally apply to other perennial forage species.

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Canopy Temperature-Based Water Stress Indices: Potential and Limitations

Cited by 19 publications

References 47 publications

An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study

An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study

Thermal imaging for assessment of maize water stress and yield prediction under drought conditions

Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass (Lolium perenneL.)—A review

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