To address the problems in the calibration of soil water content sensors, in this study, we designed a low-cost edge electromagnetic field induction (EEMFI) sensor for soil water content measurement and proposed a normalized calibration method to eliminate the errors caused by the measurement sensor’s characteristics and improve the probe’s consistency, replaceability, and calibration efficiency. The model calibration curve-fitting coefficients of the EEMFI sensors were above 0.98, which indicated a significant correlation. The experimental results of the static and dynamic characteristics showed that the measurement range of the sensor varied from 0% to 100% saturation, measurement accuracy was within ±2%, the maximum value of the extreme difference of the stability test was 1.09%, the resolution was 0.05%, the delay time was 3.9 s, and the effective measurement diameter of the EEMFI sensor probe was 10 cm. The linear fit coefficient of determination of the results was greater than 0.99, and the maximum absolute error of the measurement results with the drying method was less than ±2%, which meets the requirements of soil water content measurement in agriculture and forestry fields. The field experiment results further showed that the EEMFI sensor can accurately respond to changes in soil water content, indicating that the EEMFI sensor is reliable.

Plant vitality is an important indicator of plant health. Previous studies have often assessed plant vitality using related physiological parameters, but few studies have examined the effects of changes in plant vitality on stem water content (StWC), which can be measured online, in real time, and nondestructively using a novel fringing impedance sensor. In the present study, the sensor calibration results showed a linear fitting relationship between the sensor output voltage and StWC, with coefficients reaching 0.96. The coefficients of correlations between StWC and four plant physiological parameters related to plant vitality (net photosynthetic rate, transpiration rate, stomatal conductance, and intercellular carbon dioxide concentration) were more than 0.8, indicating that StWC can be used to characterize plant vitality to a certain extent. A comparison between plants with normal vitality and weakened vitality showed that the self-regulation ability of plants gradually weakened as the plant vitality decreased, the diurnal mean of StWC lowered, and the diurnal range of StWC increased. In conclusion, StWC can be used as a new parameter to assess plant vitality.

Current deep learning-based domain generalization (DG) methods for fault diagnosis primarily focus on learning the correlation between features and labels while neglecting the causality between them. Moreover, in equipment fault diagnosis, the quality of feature extraction directly determines model performance, and extracting better time-series features remains a key challenge. To address these challenge, we propose a causal deep domain generalization model with strong temporal feature extraction capabilities (CWSTM) for equipment fault diagnosis. First, a Selective Feature Extraction (SFE) module is constructed, which integrates MambaV2 to build a feature extraction framework from the perspective of time-varying systems. This significantly enhances the model's adaptability to operating condition variations. Second, a Structured Causal Factorization (SCF) module is designed. Here, domain-related factors such as rotational speed and load are defined as non-causal elements, while fault-related factors are treated as causal elements. By incorporating causal intervention mechanisms, we formulate a causal factorization loss function and optimize SFE module by minimizing this loss to extract invariant causal representations containing fault information. Finally, the causal factors already learned for fault diagnosis are fed into a fully connected layer for fault classification and minimizes the diagnostic classification loss to optimize the classifier and to improve diagnostic accuracy. We have designed three sets of experiments with varying domain gaps using the Case Western Reserve University Bearing Vibration Dataset (CWRU dataset) and a subway train transmission system dataset (STTS dataset). Through comparative and ablation experiments, we have validated the effectiveness of our approach. This method will enhance fault diagnosis accuracy in complex working environments, contributing significantly to the advancement of intelligent maintenance in industrial equipment.

Advancements in detection instruments have enabled the real-time acquisition of water information during plant growth; however, the real-time monitoring of freeze–thaw information during plant overwintering remains a challenge. Based on the relationship between the change in the water–ice ratio and branch impedance during freezing, a miniature noninvasive branch volume ice content (BVIC) sensor was developed for monitoring real-time changes in volumetric ice content and the ice freeze-thaw rate of woody plant branches during the overwintering period. The results of the performance analysis of the impedance measurement circuit show that the circuit has a lateral sensitivity range, measurement range, resolution, measurement accuracy, and power consumption of 0–35 mm, 0–100%, 0.05%, ±1.76%, and 0.25 W, respectively. The dynamic response time was 0.296 s. The maximum allowable error by the output voltage fluctuation, owing to the ambient temperature and humidity, was only ±0.635%, which meets the actual use requirements. The calibration curve fit coefficients were >0.98, indicating a significant correlation. The ice content of plant branches under cold stress was measured for indoor and field environments, and the sensors could effectively monitor changes in the branch ice content in plants exposed to cold stress. Additionally, they can differentiate between plants with different cold resistances, indicating the reliability of the BVIC sensor.

Soil water sensors based on the standing wave rate (SWR) principle are affected by temperature in long-term operation. To address this problem, a temperature compensation model based on the binary regression analysis method is proposed. The measurement results of the temperature-compensated standing wave rate (TCSWR) sensor at different temperatures and soil volumetric water content are analyzed, and the least-squares principle is used to identify the parameters to be determined in the compensation model for temperature for the SWR soil water sensor. A portable tapered TCSWR sensor with built-in temperature compensation model was developed on this basis. The calibration results show that the standing wave measurement circuit of the TCSWR sensor can effectively respond to changes in soil water, and the coefficient of the fitted equation exceeds 0.95. A comparison of the results before and after temperature compensation proves that compensation can significantly reduce the measurement error of the TCSWR sensor and improve the measurement accuracy. The static and dynamic characteristics of the TCSWR sensor show that the measurement range of the TCSWR sensor is7.50%-31.50%, the measurement accuracy is ±0.63%, the stability is good, the resolution is a minimum of 0.05%, and the dynamic response time is less than 1 s. The absolute error of the TCSWR sensor measurement is less than 1% in comparison with similar sensors, demonstrating that the measurement results of the TCSWR sensor are reliable.