Plant Electrical Signals Research Articles

Impacts of Epihomobrassinolide and Thiamethoxam·Flutolanil·Azoxystrobin on the Continuous Cropping Stress of Pinellia ternata

Continuous cropping (CC) stress severely limits the growth and industrial development of Pinellia ternata. Epihomobrassinolide (EBR) is a natural product that widely participates in many the physiological activities of many plants. Thiamethoxam·flutolanil·azoxystrobin (TFA) has been registered as a seed coating agent in crop production. In this work, the effects of seeds soaked with EBR, seeds coated with TFA, and their co-application on the plant growth, electrophysiological information (as physiological activities related to plant electrical signals), leaf photosynthesis, plant resistance, bulb quality, and yield of CC P. ternata were evaluated. The aim of this work is to excogitate a practicable agronomic measure for ameliorating the growth of CC P. ternata. The results show that soaking the seeds with EBR or coating the seeds with TFA could effectively enhance the plant height, leaf area, and stem diameter of CC P. ternata, promote its emergence seedling ratio, and decrease its inverted seedling ratio, and their associated application was found to be more efficient. Additionally, their associated application effectively enhanced the intrinsic capacitance (IC), intracellular water metabolism, nutrient transport, and metabolic activity and decreased the intrinsic resistance (IR), impedance (IZ), capacitive reactance (IXc), and inductive reactance (IXL). Meanwhile, their associated application could reliably enhance the photosynthetic capacity and stress resistance, and effectively improve the bulb quality and yield. This study emphasizes that the associated application of seeds soaked with a 0.004% aqueous EBR solution diluted 1000 times and seeds coated with a 24% TFA flowable concentrate at 1.6 mL kg−1 seed can be used as a novel and practicable technology for alleviating the CC stress of P. ternata and ameliorating its growth, electrophysiological information, resistance, quality, and yield.

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

Realistic scenarios produce labeled data and unlabeled data, however, there are significant challenges in labeling time series data. It is imperative to effectively integrate the relationship between labeled and unlabeled data within semi-supervised classification model. This paper presents a novel semi-supervised classification method, namely Data Augmentation-Fast Shapelet Semi-Supervised Classification, which employs a data augmentation module to enhance the diversity of data and improve the generalization ability of the model, as well as a feature fusion module to enhance the semi-supervised network. A conditional generative adversarial network is used to synthesize excellent labeled time series samples to enhance the homogeneous data in the sample space, the fast shapelets method is used to quickly extract the important shape feature vectors in the time series, self-supervised and supervised learning are combined to fully learn the unlabeled and labeled data of the time series dataset. Thejoint loss function combines the loss functions of the two networks to optimize multiple objectives. Reinforcement learning is used to determine the weight coefficients of the joint loss function, at the same time, the reward function is modified to bias the supervisory loss, which improves the classification performance of the model under limited labeled data, and the model can also better achieve the semi-supervised classification task. The proposed method is validated on the UCR benchmark dataset, Electrocardiogram dataset, and Electroencephalogram dataset, the results show that the semi-supervised classification method can perform a more accurate semi-supervised classification of the time series, with an accuracy better than the comparison methods. Meanwhile, we use the plant electrical signal dataset obtained from actual measurements for testing, the visualizationanalysis can clearly show the model role in the semi-supervised classification task, and the experimental results fully demonstrate the effectiveness and applicability of the proposed method.

Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

Early detection of dark-affected plant mechanical responses using enhanced electrical signals

BackgroundMechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields. However, the main bottleneck is the low-throughput nature of plant electrical signals, making it challenging to obtain a substantial amount of training data. Consequently, training these models with small datasets often leads to unsatisfactory performance.ResultsIn the present work, we set out to decode wound-induced electrical signals (also termed slow wave potentials, SWPs) from plants that are deprived of light to different extents. Using non-invasive electrophysiology, we separately collected sets of local and distal SWPs from the treated plants. Then, we proposed a workflow based on few-shot learning to automatically identify SWPs. This workflow incorporates data preprocessing, feature extraction, data augmentation and classifier training. We established the integral and the first-order derivative as features for efficiently classifying SWPs. We then proposed an Adversarial Autoencoder (AAE) structure to augment the SWP samples. Combining them, the Random Forest classifier allowed remarkable classification accuracies of 0.99 for both local and systemic SWPs. In addition, in comparison to two other reported methods, our proposed AAE structure enabled better classification results using our tested features and classifiers.ConclusionsThe results of this study establish new features for efficiently classifying wound-induced electrical signals, which allow for distinguishing dark-affected local and systemic plant wound responses. We also propose a new data augmentation structure to generate virtual plant electrical signals. The methods proposed in this study could be further applied to build models for crop plants using electrical signals as inputs, and also to process other small-scale signals.

Synergistic Effects of Bicarbonate and Selenium on Cadmium Transport in Karst-Adaptable Plants Based on Plant Electrical Signals

High cadmium (Cd) concentrations associated with geochemical anomalies are prevalent in carbonate-rich karstic areas, posing serious ecological risks, while the karstic soils are rich in bicarbonate (HCO3−). It is known that Selenium (Se) is a mineral element that effectively mitigates the Cd transport in plant species. However, the synergistic effects of HCO3− and Se on Cd translocation in plant species have not yet been indicated. In this study, based on plant electrophysiological measurements, we chose Orychophragmus violaceus (Ov) as experimental material and determined the growth potential, water metabolism, and nutrient translocation capacity under HCO3−-Se6+-Cd2+ treatment to identify the synergistic effects of bicarbonate and selenium on cadmium transport in karst-adaptable plants. We found 5 mM HCO3− and 0.46 mM Se6+ synergistically alleviated the Cd2+ stress on Ov, promoting growth, intracellular water metabolism, nutrient translocation efficiency, and total Se accumulation, and inhibiting the uptake of Cd in Ov. Nevertheless, 15 mM HCO3− and 0.46 mM Se6+ synergistically augmented Cd transport on Ov, resulting in growth reduction, intracellular water metabolism, nutrient translocation efficiency, and total Se concentration. Hence, it not only provides a frontier approach for the real-time monitoring of intracellular water and nutrient utilization capacity in plant species, but is also even more a theoretical idea that reveals the synergistic effects of bicarbonate and selenium on cadmium transport of plants to precisely indicate selenium supplementation to ameliorate cadmium pollution and construct a sustainable karst ecosystem in the future.

Analysis Of Low Offset Operation Amplifier Principles and Its Application Areas

While the functionality of traditional operational amplifiers today is no longer sufficient in areas that require high accuracy, low offset operational amplifiers are used in a wide range of applications in today's world. Compared with traditional operational amplifiers, low offset operational amplifiers have a series of advantages such as high precision and high signal-to-noise ratio, and are currently used in ECG signal acquisition, laser ranging, and plant electrical signal acquisition. Low offset op amps can amplify the signal with more accurately, which is advantageous in environments where the acquired signal is weak and the noisy is large, It mainly reduces the system offset by adjusting the input stage circuit (using the three-stage amplification circuit as the preamplifier circuit and using the two-stage amplification to prevent saturation of the preop amp), and improves the manufacturing process to reduce the structural offset to ensure the accuracy of the output signal. In the future, high integration and high accuracy will be the main development direction of operational amplifiers.

Liquid metal-based plant electronic tattoos for in-situ monitoring of plant physiology

Flexible plant sensors, as a noninvasive and real-time monitoring method for plant physiology, are becoming crucial for precision agriculture. However, integrating flexible devices with plants are challenging due to their fragility and complex surfaces. In this study, we introduce a liquid metal-based plant electronic tattoo (LM-PET) that can harmlessly and continuously monitor the loss of water content and plant electrical signals, which are critical parameters for analyzing plant physiological status. The LM-PET achieves double-sided conductivity through soluble electrostatic spinning films and transferring technology, effectively addressing the issue of mismatch between the rigid interface of electronic devices and the surface of delicate plants. The fabricated tattoo electrode can adhere tightly to the leaf surface for a long time and can significantly broaden the scope of moisture monitoring, even in cases of severe wrinkling caused by water loss. At the optimum operating frequency of 100 kHz, the sensitivity of LM-PET can reach 25.4 kΩ%−1. Thus, LM-PETs can record the electrical signals generated when abiotic stresses threaten plants. They are also significant in providing a deeper understanding of the drought adaptation mechanisms of plants and developing drought-resistant varieties. They offer data-driven crop management and decision-making guidance, which is imperative for advancing precision agriculture. Overall, our findings provide valuable insights into the performance of agricultural inputs and facilitate real-time monitoring of plant growth and development.

Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data.

Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms. A comparison using reduced dimensional projection of the high dimensional features via principal component analysis (PCA) has also been presented. Since the experimental data is highly unbalanced due to varying length of the experiments, we employ a random under-sampling approach for the two majority classes to create an ensemble of confusion matrices to compare the classification performances. Along with this, three other multi-classification performance metrics commonly used for unbalanced data viz. balanced accuracy, F1-score and Matthews correlation coefficient have also been analyzed. From the stacked confusion matrices and the derived performance metrics, we choose the best feature-classifier setting in terms of the classification performances carried out in the original high dimensional vs. the reduced feature space, for this highly unbalanced multiclass problem of plant signal classification due to different chemical stress. Difference in the classification performances in the high vs. reduced dimensions are also quantified using the multivariate analysis of variance (MANOVA) hypothesis testing. Our findings have potential real-world applications in precision agriculture for exploring multiclass classification problems with highly unbalanced datasets, employing a combination of existing machine learning algorithms. This work also advances existing studies on environmental pollution level monitoring using plant electrophysiological data.

A graph neural network model for deciphering the biological mechanisms of plant electrical signal classification

The classification methods of the electrical signal can be effectively applied for the identification of salt tolerance in plants. Biotechnology can help researchers discover genes and proteins related to plant electrical signals, thereby deciphering the biological mechanisms of electrical signal classification, but it is time-consuming and expensive only using biological technologies. Although protein interaction relationships can be predicted using deep learning, the relationships between plant electrical signals and proteins are unclear. This uncertainty can directly lead to protein information incompletion or lack of plant electrical signal data, which is not conducive to prediction performance. To this end, we propose a graph neural network model that integrates plant electrical signal features to discover electrical signal-related proteins (PMESP). In particular, the model constructs protein interaction relationships as a graph structure, where nodes represent proteins. To obtain sufficient feature information of these nodes, we develop an electrical signal feature extraction model based on a bidirectional long short-term memory and utilize a pre-trained neural network to extract the semantic information features of proteins. In the PMESP, the features of the electrical signals and proteins are fused as the input of graph convolutional neural network (GCN), the representations of pairs of nodes are calculated by multilayer GCNs, and then the new protein interaction relationships are predicted by the comparison of the scores between nodes connected by edges to the scores between any pair of nodes. We finally assemble a dataset containing the relationships between electrical signals and protein interactions, employ the method of light induced rhythmic bioelectrogenesis to collect data from Arabidopsis thaliana leaves under salt stress, and use weighted gene co-expression network analysis and crawler methods to obtain Arabidopsis thaliana protein interaction relationships. The results illustrate that the area under the receiver operating characteristic curve and area under precision-recall curve values of the model on the test set are 0.9624 and 0.9684 respectively, which are significantly higher than other link predicting models and previous existing methods.

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Benchmarking organic electrochemical transistors for plant electrophysiology.

Plants are able to sense and respond to a myriad of external stimuli, using different signal transduction pathways, including electrical signaling. The ability to monitor plant responses is essential not only for fundamental plant science, but also to gain knowledge on how to interface plants with technology. Still, the field of plant electrophysiology remains rather unexplored when compared to its animal counterpart. Indeed, most studies continue to rely on invasive techniques or on bulky inorganic electrodes that oftentimes are not ideal for stable integration with plant tissues. On the other hand, few studies have proposed novel approaches to monitor plant signals, based on non-invasive conformable electrodes or even organic transistors. Organic electrochemical transistors (OECTs) are particularly promising for electrophysiology as they are inherently amplification devices, they operate at low voltages, can be miniaturized, and be fabricated in flexible and conformable substrates. Thus, in this study, we characterize OECTs as viable tools to measure plant electrical signals, comparing them to the performance of the current standard, Ag/AgCl electrodes. For that, we focused on two widely studied plant signals: the Venus flytrap (VFT) action potentials elicited by mechanical stimulation of its sensitive trigger hairs, and the wound response of Arabidopsis thaliana. We found that OECTs are able to record these signals without distortion and with the same resolution as Ag/AgCl electrodes and that they offer a major advantage in terms of signal noise, which allow them to be used in field conditions. This work establishes these organic bioelectronic devices as non-invasive tools to monitor plant signaling that can provide insight into plant processes in their natural environment.

Abiotic stress classification through spectral analysis of enhanced electrophysiological signals of plants

Electrical activity in plants undergoes potential changes in interpreting the plant's physiological state. These electrical signals show arbitrary and probabilistic dynamics in which stress can be detected and analysed in the early stages of symptom appearance in plants. Evaluation of uncertainty in plant signals by signal strength enhancement is stress-specific. In this paper, we considered physiological signals from 15 days old soybean plants that endured three types of stress (cold, osmotic and lowlight). A novel enhancement methodology is proposed to pre-process the stress information present in the electrical signals. Sixteen spectral features were computed from spectrograms of enhanced signals to attain a better hypothesis. To verify the performance metrics of pre and post-enhancement methods, classification is done in two levels (Multi-class, Binary-class) using machine learning algorithms (Decision Trees (DT), K- Nearest Neighbors (KNN), Support Vector Machines (SVM), Ensemble Boosted Tree (EBT), Ensemble Bagged Tree (EBGT), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA)). Superlative feature pairs showing classification accuracy greater than 95% after 5-fold cross-validation were nominated. The results obtained by the SVM classifier in cold stress and KNN in lowlight stress are accurate to 98% after enhancement. The successful premises taken from this work assure early-stage detection and classification of plant stress, thereby suggesting future requirements to develop enhancement & classification techniques according to the type of stress. • Plant electrical signals play a significant role in comprehending plant stresses. • Accuracies improved after processing signals by enhancement methods. • Spectral feature extraction and feature pairing structured the classification model. • Classification performance is greatly improved after enhancement. • Various ML classifiers obtained results greater than 95% in classifying abiotic stress.

Wound-Induced Systemic Responses and Their Coordination by Electrical Signals

Wounding not only induces the expression of damage-responsive genes, but also initiates physiological changes, such as tissue repair, vascular reconnection, and de novo organogenesis in locally damaged tissues. Wound-induced signals also propagate from the site of wounding to distal organs to elicit a systemic response. Electrical signaling, which is the most conserved type of systemic signaling in eukaryotes, is triggered by wound-induced membrane potential changes. Changes in membrane potential spread toward systemic tissues in synergy with chemical and hydraulic signals. Here, we review current knowledge on wound-induced local and systemic responses in plants. We focus particularly on how wound-activated plasma membrane-localized ion channels and pumps propagate systemic information about wounding to induce downstream molecular responses in distal tissues. Finally, we propose future studies that could lead to a better understanding of plant electrical signals and their role in physiological responses to wounding.

The Differential Response of Intracellular Water Metabolism Derived from Intrinsic Electrophysiological Information in Morus alba L. and Broussonetia papyrifera (L.) Vent. Subjected to Water Shortage

Plant electrical signals can quickly respond to the shifting environment. Almost all life activities of plants are dependent on water. The measurement of plant electrophysiological indices provides a more convenient method for studying the intracellular water utilization. In this study, Morus alba L. (Morus alba or M. alba) and Broussonetia papyrifera (L.) Vent. (Broussonetia papyrifera or B. papyrifera) were experimental materials, and the parameters were measured in two habitats (waterfront, well-water and arid slopes, deficient-water). The physiological and electrophysiological responses of leaves to different habitats were analyzed. The theoretically intrinsic relationships between the clamping force and leaf impedance (Z), capacitive reactance (Xc), resistance (R), and inductive reactance (Xl) were revealed as 3-parameter exponential decay and linear models based on bioenergetics, respectively. Leaf intrinsic electrophysiological parameters were successfully obtained by using the above-mentioned relationships and were used to manifest metabolic activity in plants. The intracellular water-holding capacity (IWHC), water use efficiency (IWUE), water-holding time (IWHT), and water transfer rate (WTR) of plant leaves were defined based on the intrinsic electrophysiological parameters and were used to reflect the intracellular water metabolism. The correlation between the physiological and electrophysiological parameters of the two plant species in the two habitats was also analyzed. The results showed that Morus alba continuously adapted to the shifting environment, the intracellular water metabolism was insensitive to soil water shortage and was independent from the external physiological state. The intracellular water metabolism in Broussonetia papyrifera was very sensitive to soil water shortage, and both intracellular water metabolism and immediate physiological parameters could characterize the response of Broussonetia papyrifera growth and development to soil water.

Variation characteristics of plant electrical signal and their relationship with negative air ion under different light intensities.

Negative air ion (NAI) is an essential indicator for measuring air cleanliness of a given area, with vital role in regulating psychological and physiological functions of human body. The photoelectric effect is an important source and influencing factor for the generation of NAI during photosynthesis, but the photoelectric effect is extremely weak and difficult to monitor. Plant electrical signal is an important indicator that indirectly reflects photoelectric effect. Previous studies mostly focused on the spatiotemporal variation of NAI in different forest communities and its relationship with meteorological factors. At present, there is little research on NAI and plant electrical signal. In this study, we explored the effect of different light intensities (0, 150, 300, 500, 700, 800, 1000 and 1200 μmol·m-2·s-1) on characteristics of the plant electrical signal and its relationship with negative air ion, with Pinus bungeana as the research object. The results showed that the intensity of plant electrical signal increased significantly with the increases of light intensity in the illumination range of 0-700 μmol·m-2·s-1. When light intensity reached 700 μmol·m-2·s-1, plant electrical signal activity reached the highest level, and plant was inhibited by light when light intensity increased further, with plant electrical signal activity decreased. The frequency-domain parameters (edge frequency, gravity frequency, power spectrum entropy and power spectrum peak) of plant electrical signals were significantly correlated with NAI. The correlation coefficient between edge frequency (E) and NAI was the highest, the relationship between them was NAI=30.981E+168.814 (R2=0.54), and the mean square error was 52.203. There was a significant correlation between plant electrical signals and NAI, which could characterize the change rule of NAI, and provide scientific evidence for further understanding the contribution potential and production mechanism of forest to NAI.

A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance

Screening salt-tolerant crops at different salt concentrations is of great significance but time-consuming. It remains a major challenge to automatically find the appropriate stress concentration to identify the salt tolerance of crops by using the recorded electrical signals of plants over long periods of time. To solve this problem, we designed a data-driven signal dynamics prediction model (SLSTM-TCNN), based on a one-dimensional convolutional neural network (1D-CNN) combined with a long short-term memory neural network. Furthermore, we developed a quantitative model, named the NaCl stress concentration discrimination model (SCDM), to investigate the relationship between the electrical signals, NaCl stress concentration, and time dependence, and used a salt tolerance classification model (STCM) to discover the most appropriate NaCl stress concentration for distinguishing the salt tolerance of wheat. These methods perform the time-consuming task of selecting salt-tolerant varieties of plants under different NaCl concentrations. The results show that the SLSTM-TCNN could quickly predict the signal dynamics of wheat leaves DeKang961 (salt-tolerant) and Langdon (salt-sensitive) under the ongoing stress of different NaCl concentrations. The accuracy of SCDM for differentiating NaCl stress concentrations increased to 88% and 83%, and that of STCM for classifying salt-tolerant and salt-sensitive varieties reached 92.36%. Finally, it was found that the salt tolerance of the two varieties (DeKang961 and Langdon) was higher when the NaCl concentration was in the range of 50–200 mM. In the future, the method will be a potentially useful tool for identifying the salt tolerance of other crops at the seedling stage.

Research on Plant Growth State Classification Based on CNN- LSTM

The plant electrical signal is a physiological signal that reflects the growth state of plants affected by the external environment. Online monitoring of plant growth states is realized by studying the electrical signal changes of plants in different growth states. In this paper, a Convolutional Neural Network(CNN) based and Convolutional Neural Network and Long Short-Term Memory Neural Network(CNN-LSTM) based classification model of plant growth state is built to realize feature extraction and training and classification studies of Aloe Vera electrical signals in different growth states. The short-time Fourier transform (STFT) is used to convert the de-noised aloe electrical signal into a signal energy map, which is used as the input of the classification model, and the different growth states of the aloe are used as the output of the classifier. It is concluded that the CNN-LSTM neural network model has high accuracy in the classification of aloe electrical signals in different growth states when training, and the plant electrical signals can be used as an effective evaluation index for plant growth state detection.

A comparative study on the circadian rhythm of the electrical signals of Broussonetia papyrifera and Morus alba

ABSTRACT The circadian clock regulates a wide range of physiological processes in plants. Here we showed the circadian variations of the electrical signals in Broussonetia papyrifera L. and Morus alba L. in a natural state, which were analyzed using the day–night cycle method. The circadian characteristics of different plant electrical signals were compared by constructing a coupling model for the circadian rhythm of plant electrical signals. The electrical signal sensor had two electrode plates, which were fixed on the two ends of the splint, leaves could then be clamped and measured. The clamping force between the two electrode plates was uniform, which enabled continuous and nondestructive measurements. The results showed that an electric cyclic behavior was observed (circadian cycle) with the circadian variation in the plants within 24 h. Both the resistance (R) and the impedance (Z) increased firstly in the early morning and then decreased subsequently, while the capacitance (C) showed an opposite variation. Under different weather conditions, plant electrical signals showed periodic changes when the temperature and light intensity in the environment slightly changed within the physiological tolerance of plant. This indicated that the circadian clock of plant electrical signals could be maintained endogenously. The variation curves of plant electrical signals as time increased were fitted using the sine equation. The characteristic parameters of circadian rhythm of plant electrical signals were obtained. We found that although all plant electrical signals exhibited electric cyclic behavior, but the characteristics of circadian rhythms of electrical signals were different. This study provided a scientific basic for precisely monitoring plant electrical signals, and a reference for revealing circadian rhythms of plant electrical signals and their occurrence rules.

Signaling and transport processes related to the carnivorous lifestyle of plants living on nutrient-poor soil.

In Eukaryotes, long-distance and rapid signal transmission is required in order to be able to react fast and flexibly to external stimuli. This long-distance signal transmission cannot take place by diffusion of signal molecules from the site of perception to the target tissue, as their speed is insufficient. Therefore, for adequate stimulus transmission, plants as well as animals make use of electrical signal transmission, as this can quickly cover long distances. This update summarises the most important advances in plant electrical signal transduction with a focus on the carnivorous Venus flytrap. It highlights the different types of electrical signals, examines their underlying ion fluxes and summarises the carnivorous processes downstream of the electrical signals.

Ultraconformable, Self‐Adhering Surface Electrodes for Measuring Electrical Signals in Plants

AbstractThe electrical signals in plant's physiological processes are of great interest in biology, biohybrid robotics, and sensors for interfacing the living organisms with an electronic readout and control. This paper reports on the application of conformable, self‐adhering surface electrodes for the measurement and bidirectional stimulation of electrical signals in plants. The inkjet‐printed poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate based electrodes are <3 µm thick, light‐weight, soft and flexible, and can be easily and non‐invasively transferred onto plant's outer organs for surface potential recordings due to their realization on tattoo transfer paper. The devices prove to be extremely versatile for analyzing electrical signals in Dionaea muscipula, Arabidopsis thaliana, and Codariocalyx motorius and for stimulating mechanical responses in D. muscipula. A benefit over traditional electrodes is the van der Waals self‐adherence of the thin electrodes, their intrinsic flexibility and adaptation also on small leaves while providing excellent readout. The same electrode allows long‐term multicycle measurements over at least 10 days and, moreover, straightforward recordings on fast‐moving organs such as snapping fly traps and endogenously oscillating leaflets. The results confirm that self‐adhering soft organic electronics are particularly suitable for plant electrical signal analysis when easy‐application, self‐adaptation, and long‐term performance are required in plant science, biohybrid robotics, and biohybrid sensors.