Currently, deep learning applications in tomato disease recognition are becoming widespread. However, in greenhouse tomato cultivation environments, many techniques neglect the foggy image caused by wet conditions, adversely affecting the performance of disease recognition models, which still remains unresolved. To alleviate adverse environmental impacts, such as fog, and enhance the precision and robustness of the model, this study introduces an improved dark channel prior (DCP) dehazing algorithm and proposes the TDR-Model based on the MobileNetV3. Employing the quad-tree spatial hierarchy search strategy, guided filtering, tolerance mechansim, and multi-scale color restoration (MSRCR) algorithm, an improved version of DCP dehazing algorithm is realized. Image preprocessing with the improved DCP dehazing algorithm obtains more authentic dehazed images. Moreover, MobileNetV3 is improved by the incorporation of the convolutional block attention module (CBAM) and Omni-Dimensional Dynamic Convolution(ODC), improving the accuracy of disease feature recognition. The experiment results demonstrate that, with the parameter size remaining nearly unchanged (a slight reduction of 0.04M), an increase of 16.88% is achieved in accuracy compared to the original version for foggy images.

Grouting technology is widely used in construction and civil engineering, where evaluating grouting effectiveness is crucial due to the uncertainty of subsurface conditions. Existing methods face drawbacks such as destructiveness, high cost, poor durability, and limited data collection. To address these issues, this paper proposes a novel wireless real-time monitoring system based on a ZigBee sensor network framework. The sensor system integrates a direct current method in geophysics with apparent resistivity measurement to assess grouting effectiveness in real time. It consists of multichannel data acquisition units with electrodes for sensing underground currents and a user control unit for centralized management and data processing. A system acquisition performance test confirmed that the differential input channel’s equivalent input noise of the ADC was only 175 μV and 188 μV, and the average error of the captured sine wave data was 4.51 mV and 4.19 mV, ensuring the voltage measurement accuracy of the data acquisition units. Stability testing of the equipment in road and construction environments showed an average RSD of 2.86% and 2.92%, respectively, indicating good stability of the measurements. ZigBee network performance tests in three simulated environments and a field test showed that the packet loss rate (PLR) was less than 2% from 0 to 50 m, ensuring network communication in grouting project scenarios. On-site experiments demonstrate that the system can simultaneously monitor multiple profiles and perform inversions in the grouting area, which can be assembled into 3D inversion images for evaluating grout diffusion, offering valuable insights for optimizing construction operations, and enhancing grouting efficiency.

The safe operation of gas pipelines in urban, industrial, agricultural, and other areas still faces risks and challenges. The pipeline leak detection method is aimed at improving the safety of gas pipeline operations in various fields and reducing losses caused by gas leaks. This method utilizes highly sensitive microphones to collect real-time sound signals in pipeline environments and uses the Mel-scale Frequency Cepstral Coefficients (MFCC) algorithm to extract features from the collected sound signals. Then, a convolutional neural network algorithm is used to identify whether there is a gas leak. This method can accurately identify pipeline gas leaks with up to 98.958% accuracy.

This paper introduces an embedded motor fault diagnosis system that utilizes vibration signal analysis and machine learning. The system is centered around the STM32H753 microcontroller and gathers real-time vibration signals from the motor using a vibration sensor. Signal features are extracted using Discrete Wavelet Transform (DWT) and combined with machine learning anomaly detection (AD) model for automatic monitoring of motor health status and fault identification. This system offers strong adaptability and high accuracy compared to traditional methods, making it well-suited for real-time application requirements in industrial sites.