To monitor multiple environmental factors of henhouses in modern chicken farms, a henhouse online monitoring system based on wireless sensor network was developed using wireless sensor technology and computer network technology. Sensor data compensation and correction were designed to be achieved using software and data fitting methods, data reliable transmission achieved using a data loss recovery strategy, and data missing during monitoring addressed using a self-decision and online filling method. Operation test of the system showed that: The system was economic and reliable; it enabled wireless monitoring and Web display of the environmental factors of a henhouse; and the root mean square errors (RMSEs) between the estimated values from the self-decision and on-line filling method and experimental values of the four environmental factors were 0.1698, 3.0859, 77 and 0.094, respectively, indicative of high estimation accuracy. The system can provide support for modern management of henhouses and can be transplanted to related monitoring scenarios in the agricultural field.

This article studies the sterilization effects of high-voltage pulsed electric field (PEF) of technology on filamentous fungi. A cell dielectric model was proposed based on the physical structure of filamentous fungi. Basic theories of the electromagnetic field were comprehensively applied, and the multiphysics field simulation software COMSOL Multiphysics was used for more detailed study. The effects of PEF treatment parameters and microbial characteristic parameters on the resulting cell membrane and nuclear membrane changes were simulated and analyzed. The results showed significant effects on the transmembrane voltage of the cell membrane and nuclear membrane from the electric field intensity, pulse duration, cell membrane thickness, superposition effect of the pulses. However, the amount of hyphae had little effect, and the number of cell nuclei and the thickness of the cell walls had almost no effect on the transmembrane voltage of the cell membranes and the nuclear membranes. The results provide theoretical support for applying high-voltage PEFs to kill fungi in practical applications.

Rice seed variety purity, an important index for measuring rice seed quality, has a great impact on the germination rate, yield, and quality of the final agricultural products. To classify rice varieties more efficiently and accurately, this study proposes a multimodal l fusion detection method based on an improved voting method. The experiment collected eight common rice seed types. Raytrix light field cameras were used to collect 2D images and 3D point cloud datasets, with a total of 3194 samples. The training and test sets were divided according to an 8:2 ratio. The experiment improved the traditional voting method. First, multiple models were used to predict the rice seed varieties. Then, the predicted probabilities were used as the late fusion input data. Next, a comprehensive score vector was calculated based on the performance of different models. In late fusion, the predicted probabilities from 2D and 3D were jointly weighted to obtain the final predicted probability. Finally, the predicted value with the highest probability was selected as the final value. In the experimental results, after late fusion of the predicted probabilities, the average accuracy rate reached 97.4%. Compared with the single support vector machine (SVM), k-nearest neighbors (kNN), convolutional neural network (CNN), MobileNet, and PointNet, the accuracy rates increased by 4.9%, 8.3%, 18.1%, 8.3%, and 9%, respectively. Among the eight varieties, the recognition accuracy of two rice varieties, Hannuo35 and Yuanhan35, by applying the voting method improved most significantly, from 73.9% and 77.7% in two dimensions to 92.4% and 96.3%, respectively. Thus, the improved voting method can combine the advantages of different data modalities and significantly improve the final prediction results.

In this research, artemisia selengensis was used to produce biochar via microwave pyrolysis. The influence of pyrolysis temperature, heating rates, temperature holding time and additive on the biochar yield and adsorbability were all investigated. The results suggest that the biochar yield decreased with the increase of pyrolysis temperature while the adsorbability of the biochar increased with an increase of the pyrolysis temperature; the biochar yield and its adsorbability could achieve the desired value when the heating rate and temperature holding time were in a specific scope; the biochar yield decreased when an additive was added; the adsorbability of the biochar could be increased by adding ZnCl₂ (metal chloride) and Na₂CO₃ (metal carbonate). According to the orthogonal experiments, the optimal conditions for biochar production were: pyrolysis temperature 550 °C, heating rate 2 °C/s, temperature holding time 15 min, without additive.

This work aimed to analyse the damaging effects of pulsed electric fields on Rhizoctonia solani. Design Expert software was used to design an orthogonal experiment. The cell membrane damage and cell wall damage were observed by scanning electron microscopy and quantitatively determined while using a conductivity metre and an ultraviolet spectrophotometer. The results showed that the cell membrane damage rate was correlated with the voltage amplitude and processing time (p < 0.01), while the effect of pulse duration was not significant (p > 0.05). Besides, the cell wall damage was related to electric field strength (voltage amplitude) (p < 0.01), while the pulse duration and processing time had no significant effect on that (p > 0.05). The optimal process parameters for this method were 25 kV/cm, 5 min., and a pulse duration of 60 µs. The optimised conditions were tested based on these results. When compared with Control Check (CK), the cell membrane damage rate was 48.72%, which was significantly higher than CK (p < 0.01).