Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

Abstract

Plant leaf movement is induced by some combination of different external and internal stimuli. Detailed geometric characterization of such movement is expected to improve understanding of these mechanisms. A metric high-quality, non-invasive and innovative sensor system to analyze plant movement is Terrestrial LiDAR (TLiDAR). This technique has an active sensor and is, therefore, independent of light conditions, able to obtain accurate high spatial and temporal resolution point clouds. In this study, a movement parameterization approach of leaf plants based on TLiDAR is introduced. For this purpose, two Calathea roseopicta plants were scanned in an indoor environment during 2 full-days, 1 day in natural light conditions and the other in darkness. The methodology to estimate leaf movement is based on segmenting individual leaves using an octree-based 3D-grid and monitoring the changes in their orientation by Principal Component Analysis. Additionally, canopy variations of the plant as a whole were characterized by a convex-hull approach. As a result, 9 leaves in plant 1 and 11 leaves in plant 2 were automatically detected with a global accuracy of 93.57 and 87.34%, respectively, compared to a manual detection. Regarding plant 1, in natural light conditions, the displacement average of the leaves between 7.00 a.m. and 12.30 p.m. was 3.67 cm as estimated using so-called deviation maps. The maximum displacement was 7.92 cm. In addition, the orientation changes of each leaf within a day were analyzed. The maximum variation in the vertical angle was 69.6° from 12.30 to 6.00 p.m. In darkness, the displacements were smaller and showed a different orientation pattern. The canopy volume of plant 1 changed more in the morning (4.42 dm3) than in the afternoon (2.57 dm3). The results of plant 2 largely confirmed the results of the first plant and were added to check the robustness of the methodology. The results show how to quantify leaf orientation variation and leaf movements along a day at mm accuracy in different light conditions. This confirms the feasibility of the proposed methodology to robustly analyse leaf movements.

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series

“…This definition is suitable for longer term phenomena such as inter- (Liang et al, 2012; Culvenor et al, 2014) and intra (Portillo-Quintero et al, 2014; Calders et al, 2015) seasonal phenology, annual biomass change (Srinivasan et al, 2014; Crommelinck and Höfle, 2016), and growth dynamics (Griebel et al, 2017). However, only a few studies using commercially available TLS systems have demonstrated that the technique is feasible for detecting physiological plant phenomena at timescales shorter by one order of magnitude or more (Puttonen et al, 2015, 2016; Zlinszky et al, 2017; Herrero-Huerta et al, 2018). Similar short timescale measurements using TLS for monitoring lava flows (Crown et al, 2013) and structural deformations (Grosse-Schwiep et al, 2013) have been demonstrated, however.…”

Vertical greenery systems: from plants to trees with self-growing interconnections

“…In different light conditions, the leaf orientation varies, and the leaves of plants are in motion to optimize the growth conditions. Based on terrestrial LiDAR, a movement parameterization of leaves can be quantified (Herrero-Huerta et al 2018). The thermal benefits of vertical forest systems highly depend on vegetation intensity and its orientation with respect to the microclimate condition between building surfaces and plants.…”

“…An appropriate selection of tree species and a proper placement of trees around buildings are important to improve benefits of trees on reducing building energy use. There are two cases (Table 3) where vertical forests have been applied as a way of improving the quality of high-rise buildings (Blanc and Lalot 2008;Giacomello 2015). All the plants and tree species growing on balconies of Bosco Verticale were selected according to the context, the expected climate conditions, orientation, solar exposition and height, summarized in Table 3.…”

“…At the same time, greening of walls of high-rise buildings can also be feasible since the ratio between surface area of walls and roofs may reach the value of 20 (Pérez et al 2014). Many countries in the world, such as the USA (Susorova et al 2013), Italy (Giacomello 2015) and Singapore (Yok 2013) have shown great interests in green infrastructures. The technologies of green infrastructures extend the potential of horizontal, vertical areas in a building that can accommodate plants, not to mention exterior and interior spaces (Tan et al 2014).…”

LiDARPheno – A Low-Cost LiDAR-Based 3D Scanning System for Leaf Morphological Trait Extraction

“…In one of the recent studies, (Jimenez-Berni et al, 2018), authors integrated the LiDAR on the high-throughput phenotyping platform, Phenomobile, to non-destructively estimate the wheat characteristics such as height, ground cover, and above-ground biomass by comparing it with RGB and NDVI data. Herrero-Huerta et al, (2018) have monitored the leaf movement activity in the indoor plant using terrestrial LiDAR, revealing the angles of leaf movement under various lightning conditions. Following the established methods in Sun et al (2017), the authors performed the in-field experiments for growth analysis for cotton plants in Sun et al (2018).…”