Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted.
- High throughput