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

Leveraging plant physiological dynamics using physical reservoir computing

Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria × ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.

Effects of NaHSO3 on Cellular Metabolic Energy, Photosynthesis and Growth of Iris pseudacorus L

According to the law of energy conservation, the energy consumed by plants to resist adversity is equal to the difference between photosynthetic energy and growth energy consumption and cellular metabolic energy in plants. The cellular metabolic energy is calculated based on the electrical signals in plants. This study mainly investigated the effect of NaHSO3 on the growth and energy traits of the aquatic plant Iris pseudacorus L. and explored the effect of NaHSO3 on energy consumption in the process of plant development. In this study, NaHSO3 was used for simulating sulfur pollution in water medium. During the 20-day experiment period, the response of I. pseudocorus to the polluted water sources simulated by adding different concentrations of NaHSO3 (0, 0.5, 2, 4, 10 mmol·L−1) was monitored, and the internal mechanism of the relationship between the forms of energy and the removal of sulfur pollution was analyzed. After the 20-day exposure experiment, the growth and nutrient absorption capacity were significantly inhibited, and this inhibition proved to be concentration-dependent. In addition, high concentrations (4 and 10 mmol·L−1) of NaHSO3 might affect photosynthesis by disrupting cell membrane systems as it may interfere with membrane proteins and lipids and thus alter membrane integrity. Therefore, the cellular metabolic energy was increased and the sulfur absorption by I. pseudocorus was promoted under the low concentration (0.5 mmol/L−1) compared with the control, the role of NaHSO3 in promoting the growth of I. pseudocorus is much greater than its toxic effect under low concentrations. Under the hydroponic culture which contained 0.5 mmol·L−1 of NaHSO3, I. pseudocorus grew well and absorbed more sulfur. The results can be used as a reference for the cultivation of aquatic plants dealing with sulfur pollution, and dilution strategy can be set up to treat water medium that is seriously polluted with sulfur.

Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System

The rapid development of highway engineering has made slope stability an important issue in infrastructure construction. To meet the needs of green vegetation growth, ecological recovery, landscape beautification and the economy, long-term monitoring research on high-slope micrometeorology has important practical significance. Because of that, we designed and created a new slope micrometeorological monitoring and predicting system (SMMPS). We innovatively upgraded the cloud platform system, by adding an ARIMA prediction system and data-fitting system. From regularly sensor-monitored slope micrometeorological factors (soil temperature and humidity, slope temperature and humidity, and slope rainfall), a data-fitting system was used to fit atmospheric data with slope micrometeorological data, the trend of which ARIMA predicted. The slope was protected in time to prevent severe weather damage to the slope vegetation on a large scale. The SMMPS, which upgrades its cloud platform, significantly reduces the cost of long-term monitoring, protects slope stability, and improves the safety of rail and road projects.

MIRRA: A Modular and Cost-Effective Microclimate Monitoring System for Real-Time Remote Applications

Monitoring climate change, and its impacts on ecological, agricultural, and other societal systems, is often based on temperature data derived from official weather stations. Yet, these data do not capture most microclimates, influenced by soil, vegetation and topography, operating at spatial scales relevant to the majority of organisms on Earth. Detecting and attributing climate change impacts with confidence and certainty will only be possible by a better quantification of temperature changes in forests, croplands, mountains, shrublands, and other remote habitats. There is an urgent need for a novel, miniature and simple device filling the gap between low-cost devices with manual data download (no instantaneous data) and high-end, expensive weather stations with real-time data access. Here, we develop an integrative real-time monitoring system for microclimate measurements: MIRRA (Microclimate Instrument for Real-time Remote Applications) to tackle this problem. The goal of this platform is the design of a miniature and simple instrument for near instantaneous, long-term and remote measurements of microclimates. To that end, we optimised power consumption and transfer data using a cellular uplink. MIRRA is modular, enabling the use of different sensors (e.g., air and soil temperature, soil moisture and radiation) depending upon the application, and uses an innovative node system highly suitable for remote locations. Data from separate sensor modules are wirelessly sent to a gateway, thus avoiding the drawbacks of cables. With this sensor technology for the long-term, low-cost, real-time and remote sensing of microclimates, we lay the foundation and open a wide range of possibilities to map microclimates in different ecosystems, feeding a next generation of models. MIRRA is, however, not limited to microclimate monitoring thanks to its modular and wireless design. Within limits, it is suitable or any application requiring real-time data logging of power-efficient sensors over long periods of time. We compare the performance of this system to a reference system in real-world conditions in the field, indicating excellent correlation with data collected by established data loggers. This proof-of-concept forms an important foundation to creating the next version of MIRRA, fit for large scale deployment and possible commercialisation. In conclusion, we developed a novel wireless cost-effective sensor system for microclimates.