To explore the effects of pulsed electric field treatment on the germination of Scutellaria baicalensis seeds and the growth of seedlings, this study used the response surface methodology to design the working parameters of the pulsed electric field and treated and cultured Scutellaria baicalensis seeds. The results showed that the pulsed electric field treatment was beneficial for the germination of Scutellaria baicalensis seeds, improving the metabolic activity and stress resistance of seedlings. When the pulsed electric field treatment’s parameters were 0.5 kV·cm⁻¹, 120 μs, and 99 pulses, the germination potential of seeds was significantly increased by 29.25% and the germination index significantly increased by 20.65%, compared to the control. From 5th to 15th day, the activities of SOD, POD, and α-amylase in the seedlings, and the contents of Pro, soluble sugars, and soluble proteins were significantly increased, compared to the control. This study provides a theoretical basis for improving the germination and seedling growth of medicinal herbs such as Scutellaria baicalensis and their practical application in production.

Efficiently managing temperature and humidity in a pig house is crucial for enhancing animal welfare. This research endeavors to develop an intelligent temperature and humidity control system grounded in a three-way decision and clustering algorithm. To establish and validate the effectiveness of this intelligent system, experiments were conducted to compare its performance against a naturally ventilated pig house without any control system. Additionally, comparisons were made with a threshold-based control system to evaluate the duration of temperature anomalies. The experimental findings demonstrate a substantial improvement in temperature regulation within the experimental pig house. Over a 24 h period, the minimum temperature increased by 4 °C, while the maximum temperature decreased by 8 °C, approaching the desired range. Moreover, the average air humidity decreased from 73.4% to 68.2%. In summary, this study presents a precision-driven intelligent control strategy for optimizing temperature and humidity management in pig housing facilities.

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

Gynura formosana Kitam possesses beneficial properties such as heat-clearing, detoxification, and cough suppression, making it a highly nutritious plant with significant economic value. During its growth, the plant’s leaves are prone to infections that can impair stomatal function and hinder growth. Effective identification of stomatal openings and timely application of appropriate chemicals or hormones or indirect environmental adjustments (such as light, temperature, and humidity) to regulate stomatal openings are essential for maintaining the plant’s healthy growth. Currently, manual observation is the predominant method for monitoring stomatal openings of Gynura formosana Kitam, which is complex, labor-intensive, and unsuitable for automated detection. To address this, the study improves upon YOLOv8s by proposing a real-time, high-precision stomatal detection model, Refined GIoU. This model substitutes the original IoU evaluation methods in YOLOv8s with GIoU, DIoU, and EIoU while incorporating the SE (Squeeze-and-Excitation) and SA (Self-Attention) attention mechanisms to enhance understanding of feature representation and spatial relationships. Additionally, enhancements to the P2 layer improve the feature extraction and scale adaptation. The effectiveness of the Refined GIoU is demonstrated through training and validation on a dataset of 1500 images of Gynura formosana Kitam stomata. The results show that the Refined GIoU achieved an average precision (mAP) of 0.935, a recall of 0.98, and an F1-score of 0.88, reflecting an excellent overall performance. The GIoU loss function is better suited to detecting stomatal openings of Gynura formosana Kitam, significantly enhancing the detection accuracy. This model facilitates the automated, real-time monitoring of stomatal openings, allowing for timely control measures and improved economic benefits of Gynura formosana Kitam cultivation.

Efficient monitoring of pork freshness is essential to minimize spoilage-related losses in the meat industry. To address the limitations of existing detection technologies, namely high cost, poor timeliness and high environmental sensitivity, this study developed a novel electronic nose system integrating a hybrid sensor array with dynamic gas path control. By combining metal oxide semiconductor (MOS) and electrochemical sensors (e.g., MQ137, MQ136), the system exhibits high sensitivity to the key volatile organic compounds (VOCs) released during pork spoilage, achieving a detection accuracy of over 90% in identifying spoilage stages. Combined with a dual-mode gas circuit design (solenoid valve switching time: 0.85 s), the reliability of the system was further demonstrated. This technology offers an economical and efficient real-time monitoring solution for slaughterhouses and cold chain logistics, providing a new low-cost scientific approach for pork freshness assessment.

The quality of medicinal plants is closely related to the ecological factors of their growing environment, as their efficacy is reflected in the content of key medicinal components, which in turn indicates the quality of the plants. This study measured the daily variations in major constituents, including lobetyolin, polysaccharides, and total flavonoids, in Codonopsis pilosula (Franch.) Nannf., which in the Changzhi and Jincheng regions of Shanxi Province, China is known as Lu Tangshen. Throughout its growth cycle. Additionally, the study explored the effects of 11 ecological factors (both climatic and soil variables) on the primary medicinal components of C. pilosula. Through block experiments and comparisons between future data predictions and actual measurements, the reliability of the model and the consistency of block experimental data were ultimately confirmed. Principal component analysis (PCA), stepwise multiple linear regression analysis, and nonlinear polynomial modeling were employed to investigate the relationships between ecological factors and quality-related constituents (polysaccharides, total flavonoids, and lobetyolin). The results showed that linear models effectively explained daily temperature (DT) with an adjusted R² exceeding 0.8, but due to the inherently nonlinear nature of the data, it is evident that linear models are fundamentally inadequate for accurately capturing the underlying relationships. Therefore, their fit for total flavonoids and lobetyolin was suboptimal. The introduction of nonlinear polynomial models (second-, fourth-, and fifth-order) significantly improved the model fit, indicating the existence of complex nonlinear relationships between ecological factors and medicinal components. For polysaccharides, the fourth-order model demonstrated the best performance, while fifth-order models were required to adequately describe the relationships for total flavonoids and lobetyolin. Based on the best models, the optimal ranges for key ecological factors were identified: polysaccharides were best influenced by atmospheric pressure (AP) between 9.1 and 9.3 kPa, air relative humidity (ARH) between 30% and 60%, 40 cm soil mean annual temperature (40cmMAT) between 27.5 °C and 28.5 °C, soil pH between 9.68 and 9.72, and soil nitrogen (N) content between 7 and 9 mg/kg. For total flavonoids, narrow optimal ranges were observed for temperature, humidity, and pH (MAT between 10 °C and 15 °C, 40cmMAT between 27.5 °C and 28.5 °C, and pH between 9.68 and 9.72). Lobetyolin showed optimal conditions at AP of 9.1 to 9.3 kPa, 40cmMAT of 28.0 °C to 28.5 °C, ARH of 65% to 75%, pH near 9.70, and days after planting (DAP) between 10 and 50. The adoption of higher-order polynomial models clarified critical nonlinear inflection points and optimal ecological ranges, providing a refined reference for enhancing the content of medicinal components. These findings offer valuable insights for precision cultivation strategies aimed at improving the quality of C. pilosula.