Plant Stress Monitoring Research Articles

Tattoo-like Flexible Ethylene Sensor for Plant Stress Monitoring in Real-time

Tattoo-like Flexible Ethylene Sensor for Plant Stress Monitoring in Real-time

Wireless plant stresses monitoring with a wearable chemiresistor gas sensor at room temperature

Plants are susceptible to biotic and abiotic stresses, they always make morphologic and metabonomic responses for self-preservation. The volatile organic compounds (VOCs) emissions generally change significantly in the face of different external stresses. In this study, an Au interdigital electrode (IDE) chemiresistor was induced to monitor the α-pinene vapor generated from Platycladus orientalis plants in real time to determine its external stress. The composite material of polyethylene-co-vinyl acetate (PEVA)/multi-walled carbon nanotube (MWCNT) was used as the sensitive film. The sensing material showed good selectivity and sensitivity (8.32 kΩ/ppm) with the detection limit of at least 0.5 ppm, and it was hardly affected by the relative humidity. In an actual test on the P. orientalis branch, the sensor resistance got about 15% increase to 4 mm-wide bark damage and 2% increase to 40-day water stress. The near field communication (NFC) tag was integrated with the Au IDE chemiresistor, which was used as a wireless and passive switch for plant stress monitoring. This study indicated that our developed α-pinene gas sensor worked steadily in laboratory tests, and it had the potential for wireless plant stress monitoring in the field.

Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light.

Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.

Better revisiting chlorophyll content retrieval with varying senescent material and solar-induced chlorophyll fluorescence simulation on paddy rice during the entire growth stages

Solar-induced chlorophyll fluorescence at 760 nm (SIF) is a promising proxy of photosynthesis and can help improving plant stress monitoring. The Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model combines radiative transfer and enzyme kinetics of photosynthesis and is widely used to interpret SIF at different temporal and spatial scales. In this study, growing season canopy hyperspectral reflectance between 400 nm and 900 nm was used to retrieve chlorophyll content (Cab) and leaf inclination (LIDFa) using radiative transfer models (RTMs) combined with the shuffled complex evolution-University of Arizona (SCE-UA) method. These parameters were then used to simulate diurnal and seasonal trends of SIF for paddy rice. The results showed that the accuracy of Cab retrieval was improved when the variation in senescent material (Cs) was considered, especially in the later growth stages. The SCOPE model was able to reliably interpret the diurnal cycle and seasonal trend of SIF with a correlation coefficient of 0.92 and RMSE of 0.12 w m−2 sr-1 um−1. Our results revealed that the SCOPE model provides a promising method for interpreting SIF variations but its accuracy should be evaluated in different growth stages. This will serve as a significant reference for detecting plant photosynthetic activity and physiological traits at different growth stages.

A feasibility study of uninhabited aircraft systems for rapid and cost-effective plant stress monitoring at green stormwater infrastructure facilities

Abstract Vegetation health monitoring is key to identifying early signs of water stress, pollutant-induced toxicity, and plant diseases in green urban stormwater facilities. However, rigorous monitoring to collect accurate quantitative data is an expensive and time-consuming process. This paper examines the feasibility of using uninhabited aircraft systems (UAS), in comparison to standard ground-based methods, for monitoring biomass and primary production in two bioswale cells at an urban stormwater facility. Implementation of the UAS-based approach involved flight planning in an urban area to meet resolution requirements of bioswale imagery obtained from near-infrared and red-green-blue cameras. The resulting normalized difference vegetation index (NDVI) estimated from UAS data was tracked over a 2-month period during the transition from spring to summer, showing the spatial distribution of NDVI and the change in vegetation coverage areas over time. In comparison, ground-based measurements of the fraction of intercepted photosynthetically active radiation (PAR) presented multiple practical challenges during implementation in the field, leading to over- and underestimates of intercepted PAR. Overall, UAS-derived NDVI was found to be a valuable reflectance-based, vegetation health-monitoring methodology that can be used by utilities and cities for practical, cost-effective, and rapid assessment of vegetation stress and for long-term maintenance in green stormwater facilities.

Development of a Sensor Node for Remote Monitoring of Plants.

The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO2 together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants’ behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet (Ligustrum Jonandrum) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters.

Effect of Fertilization on Yield and Quality of Sisymbrium officinale (L.) Scop. Grown as Leafy Vegetable Crop

Sisymbrium officinale is a wild Brassicaceae species that is known for its use in the alleviation of vocal inflammatory states. Since this species is particularly rich in bioactive compounds, there is an interest for developing cultivation protocols to use this plant as a leafy vegetable harvested at the 13th BBCH growth stage. Two wild populations of S. officinale (L.) Scop., denominate MI (Milan) and BG (Bergamo), have been used, and three different levels of nutrients (g/m2) have been provided such as 13 N, 7 P2O5, 8 K2O (100%); 9 N, 5 P2O5, 5.5 K2O (70%); and 6.5 N, 3.5 P2O5, 4.0 K2O (50%). The effects of different fertilization levels were evaluated on the yield, leaf pigments (chlorophylls, carotenoids), nitrate concentration, sugars content, and on the antioxidant compounds such as anthocyanins, total phenols and glucosinolates (GLS). Plant stress monitoring was performed by measuring the chlorophyll a fluorescence. Results indicated that yield was not affected and ranged from 0.18 to 0.47 kg/m2, and differences were not statistically significant for chlorophylls, carotenoids, and total sugars content. Nitrate concentrations were higher in the BG wild population (4388.65 mg/kg FW) during the second cultivation cycle and lower (1947.21 mg/kg FW) in the same wild population during the first cultivation cycle, both at the 100% fertilization level. Total phenols and anthocyanins were higher in the highest fertilization rate in the MI wild population during the second cycle. The GLS were higher in the lowest fertilization rate in the BG wild population (19 µmol/g FW) grown at the fertilization level of the 50%, and significant differences were observed in the second cycle. In conclusion, the 50% fertilization level can be considered the most suitable for our experimental conditions.

A new low-cost portable multispectral optical device for precise plant status assessment

Plant stress monitoring is of crucial importance to understand the plant response to environmental conditions, and has been widely applied in various fields including biology, agronomy, botany, and horticulture. A number of methods and instruments for plant stress monitoring have been proposed, most of which show significant disadvantages such as high cost, poor accuracy or complex operation that limit the use of such devices. In this paper, we propose a low cost, portable active multispectral optical device for precise plant stress detection and field mapping named Plant-O-Meter. The device has an integrated multispectral source that comprises light sources of the four most indicative wavelengths (850, 630, 535 and 465 nm), and enables simultaneous illumination of the whole plant. Sequential illumination and detection provide rapid reflectance measurements, which are wirelessly transmitted to android operated devices for processing and data storing. The device was tested in laboratory conditions comparing the Plant-O-Meter measurements with the image results from a SPECIM hyperspectral camera and a GreenSeeker handheld device and under field conditions with the GreenSeeker. The comparison revealed comparable performance, showing strong correlation with both the hyperspectral (R2 = 0.997) and the GreenSeeker handheld (R2 = 0.954 from the laboratory measurements and R2 = 0.886 from the field experiments), indicating that the device exhibits strong potential for accurate plant stress measurements. Moreover, owing to the very simple operating principle, the device represents a compact, cost-effective, and easy-to-operate solution.

Identification of asymptomatic plants infected with Citrus tristeza virus from a time series of leaf spectral characteristics

Citrus tristeza virus (CTV) affects citrus crops with differing severity, depending on the viral strain, the citrus cultivar and the scion/rootstock combinations. In this study we address the problem of identifying asymptomatic infected plants using reflectance spectra of the leaves in the visible/near infrared region. Sixteen young citrus plants (8 Citrus×clementina hort. ex Tanaka ‘Fina’ and 8 Citrus sinensis (L.) Osbeck ‘Valencia Late’) were split into control and T318A isolate infected groups. Measurements of reflectance in the 400–1100nm range, in two leaves per plant, were performed monthly over 6months and the presence of the virus was confirmed by IC/RT-PCR and real-time PCR. The spectra acquired in a single day of measurements was inconsistent for inoculated and control plants. However, by monitoring the same leaves over 6months it was possible to identify infected plants on the basis of the spectra time evolution. In order to achieve this a simple unfolding implementation of 3-way PCA was applied such that group separation in the scores plot was spontaneous and not forced by any a priori assumption. The model was tested through leave-one-out cross validation with a good rate of correct classification for the left out sample. A real situation was simulated by applying the NPCA algorithm to healthy plants only and checking if the infected ones would be projected on the model scores plot as outliers. Again, a good rate of classification was obtained. Finally, we discuss the spectral features that may be associated with the clustering obtained through NPCA and their physiological significance. Reflectance measurements between infected and healthy samples of two citrus cultivars and their correlation with real-time PCR results for the presence of CTV suggest reflectance spectra of the leaves in the visible/near infrared region is a promising tool for plant stress monitoring linked to the presence of CTV infection prior to symptom expression.

Use of Unfold PCA for on-line plant stress monitoring and sensor failure detection

Phytodiagnostic tools are aimed at the early detection of plant and/or crop stress conditions, with a view to assisting farmers to timely intervene in order to prevent or limit plant damage. Continuous monitoring of the variations in stem diameter has attracted a lot of attention in the assessment of the plant's response to stress conditions, but on-line data acquisition leads to large datasets. Unfold Principal Component Analysis (UPCA) is a powerful and conventional tool for intelligent handling of large datasets and on-line process monitoring of batch processes in the absence of exact process knowledge. Although the use of UPCA is already common practice for certain biotechnological applications, practical applications in plant science with respect to on-line plant stress monitoring are still lacking. This work describes for the first time the application of this technique to datasets of two different plant species (i.e. young apple trees and truss tomato plants) with a view to the development of an early warning system for stress detection in plant crops. To this end, UPCA modelling was applied in such a way that a single diurnal cycle corresponds to a single batch in the original methodology. For both plant species, a PCA model was calibrated using the data of the first days of the experiment of a fault-free control plant. The remaining data of the control plant were projected onto this model for validation. Projection of experimental data of a stressed plant onto the same PCA model allowed successful stress detection, days before the appearance of visible symptoms. It can be concluded that successful alarm generation is possible with UPCA.

Two methods for the analysis of colorimetric components applied to plant stress monitoring

Abstract Several authors have shown the possibility of early detection of many stress conditions in plants by analysing leaf colour. The availability of portable instruments, increasingly accurate and reliable, leads to the belief that, in the near future, this technique could be used for routine analysis in the field. Nevertheless, due to the variability of the leaf colour parameter and consequently to the large amount of data to be collected to obtain a representative sample, direct analysis of chromaticity coordinate values, as generated by an instrument, could give results that are hard to interpret, especially when the colour variation to be detected is subtle, such as in leaves at the first stages of stress. The availability of standard methodologies to analyse colorimetric data would allow them to be interpreted correctly and, in a short time, to widen their fields of application. In this work two methods of elaboration, with related software (PCA and COLORE) that give an objective evaluation of colorimetric variations of stressed plants by means of specific indexes of alteration, are illustrated. PCA, based on principal components analysis, allows two `indexes', Tij and D, to be calculated, that can describe the differences between two or more treatments. It also allows the location of the different treatments on the chromaticity diagram to be graphically represented. COLORE calculates the values of the dominant wavelength and purity of the colour. From these, it computes the CI index that can be related to degree of stress. The two softwares were applied to data from different studies of thermal and water stress in Sorghum bicolor ((L.) Moench) and Citrus macrophylla (L). The results showed a clear correlation between the colorimetric indexes and the ecophysiological parameters chosen as indicators of stress.