A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Abstract

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36° ± 0.53° for pitch and 0.50° ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e., bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning

“…The temporal variation in leaf angles greatly differed between A. pseudoplatanus and T. cordata as expected for functionally different species (Geldhof et al, 2021). We also observed considerable departures of individual time series obtained with camera positions at top and within crowns of the assessed tree individuals.…”

“…Yet, these correlations do not necessarily imply that all short‐term variations in leaf angles can be implicitly explained by short‐term environmental effects. It is likely that a considerable oscillation in leaf angles are hard‐coded nastic movements and follow the circadian rhythm, which, in turn, can be synchronized with the prevailing oscillations of several environmental drivers (Geldhof et al, 2021; Greenham & McClung, 2015; McClung, 2006), such as solar radiation, temperature or relative humidity (Figure 7). The random forest variable importance suggests that most of the included environmental variables had a considerable effect on predicting leaf angles (Figure 7).…”

“…the course of the sun), adhesion of precipitation, active and passive responses to drought and heat stress such as drooping or wilting of leaves (Apelt et al, 2017; Niinemets, 2010; Puglielli et al, 2017). Such imprints of environmental stimuli on leaf angular orientations differ considerably between growth forms, species and even genotypes (Geldhof et al, 2021).…”

Waterlogging shifts ontogenic hormone dynamics in tomato leaves and petioles

“…Throughout the waterlogging treatment and during reoxygenation, leaf angle dynamics were monitored in real-time using leaf angle sensors (Geldhof et al, 2021).…”

Melatonin administration decrypts the inferential determinants of idiosyncratic foliar nastic movements

“…Additionally, oscillating behaviours in tissues may result from purposefully enhancing mechanical cue fluctuations. Leaf nastic motions such as proprioception may encourage organ flattening (Geldhof et al 2021). According to studies, fluctuation-enriched proprioception enables plant tissues to perceive other organisms and adapt to changing internal or external settings with flexible outputs (Moulia et al ., 2021).…”