Abstract
The consequences of changes in spatial resolution for application of thermal imagery in plant phenotyping in the field are discussed. Where image pixels are significantly smaller than the objects of interest (e.g., leaves), accurate estimates of leaf temperature are possible, but when pixels reach the same scale or larger than the objects of interest, the observed temperatures become significantly biased by the background temperature as a result of the presence of mixed pixels. Approaches to the estimation of the true leaf temperature that apply both at the whole-pixel level and at the sub-pixel level are reviewed and discussed.
Highlights
For most applications of thermal imaging or thermal sensing in plant science, especially in relation to high throughput plant phenotyping, we are interested in determining leaf temperature as an indicator of plant water deficit stress or as a measure of transpiration rate or stomatal conductance [1]
The field of view of the sensor is determined by the optics—a wider field of view will tend to increase the problem of mixed pixels
This review has briefly outlined the main ways in which image scale is important for the use of thermal imaging of plant leaves and canopies in the field for phenotyping purposes
Summary
For most applications of thermal imaging or thermal sensing in plant science, especially in relation to high throughput plant phenotyping, we are interested in determining leaf temperature as an indicator of plant water deficit stress or as a measure of transpiration rate or stomatal conductance [1]. The smaller that pixels are in relation to the objects being imaged, as with high resolution thermal images, the easier it is to extract the temperature of the leaves alone. The pixels (or field of view of the thermal sensors) are large in relation to the objects, a large proportion of pixels are mixed as they include both leaf and background, especially at the edges of objects or in low resolution images. This problem increases as the spatial resolution of the image becomes coarser to a point where the pixel size approaches or becomes greater than the object size. As we shall see below, with RGB or multi/hyper-spectral images further information is available that can be used for spectral unmixing [5,6] which allows the estimation of the proportion of different component surfaces represented in any pixel
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