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Excellent performance has been demonstrated in implementing challenging agricultural production processes using modern information technology, especially in the use of artificial intelligence methods to improve modern production environments. However, most of the existing work uses visual methods to train models that extract image features of organisms to analyze their behavior, and it may not be truly intelligent. Because vocal animals transmit information through grunts, the information obtained directly from the grunts of pigs is more useful to understand their behavior and emotional state, which is important for monitoring and predicting the health conditions and abnormal behavior of pigs. We propose a sound classification model called TransformerCNN, which combines the advantages of CNN spatial feature representation and the Transformer sequence coding to form a powerful global feature perception and local feature extraction capability. Through detailed qualitative and quantitative evaluations and by comparing state-of-the-art traditional animal sound recognition methods with deep learning methods, we demonstrate the advantages of our approach for classifying domestic pig sounds. The scores for domestic pig sound recognition accuracy, AUC and recall were 96.05%, 98.37% and 90.52%, respectively, all higher than the comparison model. In addition, it has good robustness and generalization capability with low variation in performance for different input features.

The acoustic characteristics of underwater cylindrical Helmholtz resonator are analyzed theoretically. Based on the theories of electro-acoustic analogy, a low frequency lumped-parameter model of the Helmholtz resonator is constructed with due consideration of the effects of the elasticity and the radiation impedance of the resonator. To our knowledge, this is the first time such a complete model is constructed. The input impedance and the transfer function of the system are given by circuit analysis. The effects of parameter values of the resonator on the acoustic characteristics are studied by numerical method. Some useful conclusions are drawn. A small aluminum cylindrical Helmholtz resonator is measured in a standing-wave tube filled with water. Error analysis is made in detail. The experimental results are in agreement with the simulation results considering the effect of the piezoelectric hydrophone. The validity of the theoretical analysis is testified. This paper supplies a theoretical and experimental basis for the design of underwater cylindrical Helmholtz resonators, and is useful for the estimation of underwater acoustic performance of Helmholtz resonators of other shapes.

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Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.