Gloxinia—An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.

[1]  Marie-Pierre Hiel,et al.  Impact of spatio-temporal shade dynamics on wheat growth and yield, perspectives for temperate agroforestry , 2017 .

[2]  W. Maes,et al.  Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. , 2012, Journal of experimental botany.

[3]  Guilherme N. DeSouza,et al.  Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping , 2017, Sensors.

[4]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[5]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[6]  T. Pridmore,et al.  Plant Phenomics, From Sensors to Knowledge , 2017, Current Biology.

[7]  Falk Schreiber,et al.  HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.

[8]  P. K. Dixon,et al.  Broadband digital lock-in amplifier techniques , 1989 .

[9]  Frederick C. Meinzer,et al.  Potential errors in measurement of nonuniform sap flow using heat dissipation probes. , 1999, Tree physiology.

[10]  M. Hill,et al.  Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland , 2008 .

[11]  Kathy Steppe,et al.  Stem diameter variations as a versatile research tool in ecophysiology. , 2015, Tree physiology.

[12]  Jeffrey W. White,et al.  Development and evaluation of a field-based high-throughput phenotyping platform. , 2013, Functional plant biology : FPB.

[13]  François Chaumont,et al.  A Hydraulic Model Is Compatible with Rapid Changes in Leaf Elongation under Fluctuating Evaporative Demand and Soil Water Status1[C][W][OPEN] , 2014, Plant Physiology.

[14]  A. Walter,et al.  Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.

[15]  Kathy Steppe,et al.  Sap flow as a key trait in the understanding of plant hydraulic functioning. , 2015, Tree physiology.

[16]  Pascal Neveu,et al.  Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System , 2018, The New phytologist.

[17]  C. Fournier,et al.  High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. , 2015, The New phytologist.

[18]  K. Chenu,et al.  PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. , 2006, The New phytologist.

[19]  Menachem Moshelion,et al.  Development of synchronized, autonomous, and self-regulated oscillations in transpiration rate of a whole tomato plant under water stress , 2010, Journal of experimental botany.

[20]  M. R. Thorpe,et al.  Non-invasive approaches for phenotyping of enhanced performance traits in bean. , 2011, Functional plant biology : FPB.

[21]  Kathy Steppe,et al.  Plant sensors help to understand tipburn in lettuce , 2015 .

[22]  Abraham J. Escobar-Gutiérrez,et al.  What determines the complex kinetics of stomatal conductance under blueless PAR in Festuca arundinacea? Subsequent effects on leaf transpiration , 2010, Journal of experimental botany.

[23]  Yunbi Xu,et al.  Envirotyping for deciphering environmental impacts on crop plants , 2016, Theoretical and Applied Genetics.

[24]  Kathy Steppe,et al.  Determining reference values for stem water potential and maximum daily trunk shrinkage in young apple trees based on plant responses to water deficit , 2009 .

[25]  J. Peñuelas,et al.  Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice , 2008 .

[26]  Arno Ruckelshausen,et al.  BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding , 2013, Sensors.

[27]  A. Fabbri,et al.  Anatomical changes in persistent leaves of tissuecultured strawberry plants after removal from culture , 1986 .

[28]  Frank J Vergeldt,et al.  MRI of long-distance water transport: a comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. , 2006, Plant, cell & environment.

[29]  Hamlyn G. Jones,et al.  Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces , 1999 .

[30]  Kathy Steppe,et al.  Plant-PET Scans: In Vivo Mapping of Xylem and Phloem Functioning. , 2015, Trends in plant science.

[31]  M. M. Chaves,et al.  Thermography to explore plant-environment interactions. , 2013, Journal of experimental botany.

[32]  A. Sievers,et al.  Rapid Changes in the Pattern of Electric Current around the Root Tip of Lepidium sativum L. following Gravistimulation. , 1982, Plant physiology.

[33]  Gianni Bellocchi,et al.  Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality. , 2019, Journal of experimental botany.

[34]  T. Lawson,et al.  Stomatal Size, Speed, and Responsiveness Impact on Photosynthesis and Water Use Efficiency1[C] , 2014, Plant Physiology.

[35]  Mª Victoria Cuevas Sánchez,et al.  Irrigation scheduling from stem diameter variations: A review , 2010 .

[36]  N. Baker Chlorophyll fluorescence: a probe of photosynthesis in vivo. , 2008, Annual review of plant biology.