High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest

Jerbi, Taha; Wuyts, Nathalie; Canè, Maria Angela; Faux, Philippe-François

doi:10.1071/fp15024

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…These methods are time-consuming and can also be subjective, as they depend on the threshold chosen. However, automated segmentation techniques have recently been developed to select pure vegetation pixels in TIR images (Jerbi et al 2015).…”

Section: Data Processingmentioning

confidence: 99%

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Virlet

Sabermanesh

Sadeghi‐Tehran

et al. 2017

Functional Plant Biol.

316

210

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Abstract. Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

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Section: Data Processingmentioning

confidence: 99%

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Virlet

Sabermanesh

Sadeghi‐Tehran

et al. 2017

Functional Plant Biol.

316

210

View full text Add to dashboard Cite

show abstract

“…Ref. [9] exploited a chess-like mosaic made of squares from both aligned slave and master images. In Figure 14, we propose an alternative visualization method that allow us to clearly compare the alignments of plant organs.…”

Section: Visualization Of Successful Image Registrationsmentioning

confidence: 99%

“…This could be obtained by fusing leaf angles (for example from depth map from stereo camera) and reflectance maps. Such orientation-based reflectance has been suggested to improve thermal imaging by [9]. It is to notice that this paper only envisions the fusion of images, implying that 3D information is provided as a depth map (an image whose pixel values represent distances).…”

Section: Introductionmentioning

confidence: 99%

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Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions

et al. 2021

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Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.

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“…Intraplot variability of surface temperature can be visualized and analysed (Jones et al., ). As a shortcoming of thermal images taken at low canopy density, information from soil and canopy is mixed in the images (Jerbi, Wuyts, Cane, Faux, & Draye, ). Dry soils receiving high irradiance can heat up and reach much higher temperature than the canopy.…”

Section: Introductionmentioning

confidence: 99%

Thermal phenotyping of stomatal sensitivity in spring barley

Rischbeck

Cardellach

Mistele

et al. 2017

J Agronomy Crop Science

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Thermometry and thermography are alternative methods used for measuring stomatal conductivity via transpirative cooling. However, the influence of mixed soil–plant information contained in thermal images compared to thermometric spot measurements on the measurement quality and relationships to agronomic traits remains unclear. To evaluate their respective influence, canopy temperature was measured simultaneously by two infrared thermometers (thermometry), which were oriented oblique to the plant canopy and mounted on a tractor, and a hand‐held, nadir oriented thermal camera (thermography) in irrigated and drought‐stressed spring barley cultivar trials in 2011. Canopy temperatures were separated from soil temperatures and extracted from the thermal images by matching thermal and RGB images. Thermometric measurements conducted at the beginning of shooting during a stable period of high radiation were more closely related to total plant biomass and straw yield at harvest than thermography under both irrigated and drought‐stressed conditions. Taking into account the results of this evaluation, thermometry was used for assessing the agronomic importance of stomatal sensitivity, the earliness of stomatal closure, of spring barley cultivars subjected to different water supply in 2013. In this year, 16 spring barley cultivars were grown under mild drought stress and rainfed conditions. A stomatal sensitivity index was derived relating canopy temperatures of the cultivars grown under rainfed and drought‐stressed conditions to each other. Under rainfed conditions, stomatal sensitivity was negatively related to grain protein yield with a coefficient of determination of R2 = .43. Under increasing terminal drought stress, positive regression slopes of stomatal sensitivity to grain yield, biomass yield and culms/m2 were observed with coefficients of determination amounting to R2 = .22, .31 and .36, respectively. Stomatal sensitivity negatively impacts agricultural production under well‐watered conditions, but maintains productivity under conditions of terminal drought.

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High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest

Cited by 11 publications

References 23 publications

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions

Thermal phenotyping of stomatal sensitivity in spring barley

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