(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.

Kernel moisture content is an important factor in mechanized corn harvesting. However, rapid and nondestructive in situ detection of the kernel moisture content within an intact corn ear in the field remains challenging. In this work, we designed a novel handheld device with a double-ring sensing probe for nondestructive and convenient kernel moisture detection in intact corn ears based on an active reflection bridge operating in the radiofrequency range. By proposing an equivalent first-order resistor-capacitor impedance model for a load composed of a corn ear and the double-ring sensing probe, we found that the reflection coefficient’s phase angle θ was monotonic with the moisture content and also found that the measurement signal frequency affected the corn grain moisture measurement sensitivity. Before using the handheld device in the field, we investigated its optimal signal frequency range and the effects of temperature variations on the measurement results. In the 140 MHz–165 MHz frequency range, the measured voltages from the device showed a good linear relationship and high sensitivity with respect to both the dielectric constants and the corn ear temperature (9–22°C). Using this device at a signal frequency of 160 MHz, we conducted corn ear moisture measurements in a total of 73 samples and then established a linear model. The results showed that for corn ear moisture contents in the 15.7%–31.5% range, the R2 values of the training set and the prediction set were 0.7027 and 0.8317, respectively, with a prediction error of 3.54% at the 95% confidence interval.

The limitations of conventional extracellular recording and intracellular recording make high-resolution multisite recording of plant bioelectrical activity in situ challenging. By combining a cooled charge-coupled device camera with a voltage-sensitive dye, we recorded the action potentials in the stem of Helianthus annuus and variation potentials at multiple sites simultaneously with high spatial resolution. The method of signal processing using coherence analysis was used to determine the synchronization of the selected signals. Our results provide direct visualization of the phloem, which is the distribution region of the electrical activities in the stem and leaf of H. annuus and verify that the phloem is the main action potential transmission route in the stems of higher plants. Finally, the method of optical recording offers a unique opportunity to map the dynamic bioelectrical activity and provides an insight into the mechanisms of long-distance electrical signal transmission in higher plants.

Electrical impedance tomography (EIT) has been used by researchers across several areas because of its low-cost and no-radiation properties. Researchers use complex conductivity in bioimpedance experiments to evaluate changes in various indicators within the image target. The diverse volumes and edges of biological tissues and the large impedance range impose dedicated demands on hardware design. The EIT hardware with a high signal-to-noise ratio (SNR), fast scanning and suitable for the impedance range of the image target is a fundamental foundation that EIT research needs to be equipped with. Understanding the characteristics of this technique and state-of-the-art design will accelerate the development of the robust system and provide a guidance for the superior performance of next-generation EIT. This review explores the hardware strategies for EIT proposed in the literature.

Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes’ electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit’s output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R² values for the quadric regression equation of 0.7853 and 0.8496.