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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.

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BACKGROUND: Mechanical 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. RESULTS: In 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. CONCLUSIONS: The 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.

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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.

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli--sodium chloride (NaCl), sulfuric acid (H2SO4) and ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

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