Table 1.

Methodological review of possible methods to model structural plant dynamics in outdoor conditions.

	Common		Method specific			Example use cases	References
Technique	Strengths	Weaknesses	Name	Strengths	Weaknesses
Laser scanning	Direct 3D information acquisition; Data acquisitions not limited by external lighting conditions	Data acquisition times—sensitivity to wind; Internal occlusions in the object; Single wavelength only	Presented framework	Straight forward processing; Limited parameter number; Cluster movement tracking	Does not separate individual plant parts; No direct cluster shape detection	Circadian rhythm monitoring of plants over wide area	–
			Height percentiles	Fast to perform; No parametrization	Cannot track specific plant part movements; Overgeneralizes the movement patterns	Circadian rhythm monitoring of plants over wide area	Puttonen et al., 2016; Zlinszky et al., 2017
			Quantitative structure modeling (QSM)	Robust branch and stem estimations; Plant part volume and length estimations; Plant part movement tracking	Work best in leaf-off conditions; Robustness to the internal occlusions; Computationally heavy	Accurate estimates for tree stem and branch length, diameter, and volume in forestry and ecological applications	Raumonen et al., 2013; Hackenberg et al., 2015
			Skeleton modeling	Robust branch and stem length and angle estimations; Plant part movement tracking	Work best in leaf-off conditions; Robustness to the internal occlusions; No volume information	Localized plant point cloud registrations between separate data acquisitions; Phenological trait estimation in plants and trees	Bucksch and Khoshelham, 2013; Wu et al., 2019
Imaging	Individual image acquisition nearly instantaneous; Multiple wavelength bands	Sensitivity to external lighting conditions; Range information not directly available (planar geometry); Weak penetration through the canopy surface	Structure from motion	High density 3D surface models; Individual plant part monitoring	Wide area coverage difficult (high overlap required); Computationally heavy	Phenotype parameter reconstruction; Leaf parameter estimation	Li et al., 2013; Duan et al., 2016; Hui et al., 2018
			RGB	Affordable instrumentation	Wavelength band number	Time lapse generation of circadian movements	Gooch et al., 2004
			Multi- and hyperspectral	Plant part differentiation performance	Lower resolution; Slower acquisition	Plant health estimates; Detection of active sites	Pan et al., 2015
			Thermal	Can measure in dark; Can show plant processes not visible and near-infrared wavelengths	Low resolution	Heliotropism monitoring in sunflowers	Atamian et al., 2016

The method review is not exhaustive.