Table 1.

Summary of available imaging sensors in plant phenotyping, including their advantages and related challenges.

sensor	traits measured	advantages	challenges	reviewed by
MRI	water status, transportation, and root architecture	three-dimensional architecture	low throughput and high cost	Pflugfelder et al. [23]
thermal	leaf/canopy temperature	temperature changes indicates water stress	highly influenced by environmental factors	Xie & Yang [24]
LIDAR	height and canopy architecture	high data resolution, can be operated at night	vast volumes of data, difficult analysis	Lin [25]
visible imaging (RGB)	root/shoot biomass, morphology, colour	low cost, monitoring of biomass, morphometry, and yield traits	unable to detect changes in water content or subtle	Li et al. [11]
hyperspectral imaging	traits vary depending on wavelength range of the sensor (examples include pigment concentration water content and plant nutrients); several spectral indices available (e.g. NDVI)	larger range of wavelengths, capturing stress signals before becoming visible	creates vast amounts of data; requires data mining and ML to improve data analysis	Liu et al. [26,27]
chlorophyll fluorescence	photosystem II activity	changes in ChF can occur before most other signs of stress	dark adapted measurements required	Maxwell & Johnson [28]
X-ray CT	root architecture	high-resolution, three-dimensional architecture	low automation and low throughput, high cost	Tracy et al. [29]
PET	translocation and transport of elements	shows movement and path of positron through the plant	low throughput, high cost	Garbout et al. [30]