Root growth in the field is often slowed by a combination of soil physical stresses, including mechanical impedance, water stress, and oxygen deficiency. The stresses operating may vary continually, depending on the location of the root in the soil profile, the prevailing soil water conditions, and the degree to which the soil has been compacted. The dynamics of root growth responses are considered in this paper, together with the cellular responses that underlie them. Certain root responses facilitate elongation in hard soil, for example, increased sloughing of border cells and exudation from the root cap decreases friction; and thickening of the root relieves stress in front of the root apex and decreases buckling. Whole root systems may also grow preferentially in loose versus dense soil, but this response depends on genotype and the spatial arrangement of loose and compact soil with respect to the main root axes. Decreased root elongation is often accompanied by a decrease in both cell flux and axial cell extension, and recent computer-based models are increasing our understanding of these processes. In the case of mechanical impedance, large changes in cell shape occur, giving rise to shorter fatter cells. There is still uncertainty about many aspects of this response, including the changes in cell walls that control axial versus radial extension, and the degree to which the epidermis, cortex, and stele control root elongation. Optical flow techniques enable tracking of root surfaces with time to yield estimates of two-dimensional velocity fields. It is demonstrated that these techniques can be applied successfully to time-lapse sequences of confocal microscope images of living roots, in order to determine velocity fields and strain rates of groups of cells. In combination with new molecular approaches this provides a promising way of investigating and modelling the mechanisms controlling growth perturbations in response to environmental stresses.

Automatic semantic summarisation of human activity and detection of unusual inactivity are useful goals for a vision system operating in a supportive home environment. Learned models of spatial context are used in conjunction with a tracker to achieve these goals. The tracker uses a coarse ellipse model and a particle filter to cope with cluttered scenes with multiple sources of illumination. Summarisation in terms of semantic regions is demonstrated using acted scenes through automatic recovery of the instructions given to the actor. The use of 'unusual inactivity' detection as a cue for fall detection is also demonstrated.