Temporal-Spatial Fuzzy Deep Neural Network for the Grazing Behavior Recognition of Herded Sheep in Triaxial Accelerometer Cyber-Physical Systems

The rapid development of agricultural cyber-physical systems sheds new light on facilitating agricultural production. The grazing behavior recognition of herded sheep is a paramount issue in animal husbandry. Triaxial accelerometers of agricultural cyber-physical systems provide fine-grained observations of herded sheep but also generate temporal-spatial correlated acceleration data with inherently large-scale dimensions and massive volumes. These inherent characteristics of the data constrain the direct application of existing recognition algorithms. Motivated by the unique features of triaxial accelerometers of agricultural cyber-physical systems, we design a hybrid temporal-spatial fuzzy deep neural network (TSFDNN) approach for predicting the grazing behaviors of herded sheep. We first extract temporal-spatial features and reduce data dimensionality using bidirectional long short-term memory network (Bi-LSTM) and convolutional neural network (CNN) in parallel, then control feature dimensions through principal component analysis (PCA), and finally use fuzzy neural network (FNN) to achieve feature enhancement and category mapping. The superiority of the designed TSFDNN is demonstrated through its empirical comparison with other state-of-the-art machine learning algorithms by using two datasets from sheep pastures. Furthermore, we analyze the rationale of each component in the designed TSFDNN by performing several ablation studies. We also conduct robustness experiments with heterogeneous dimension reduction and optimization algorithms to explore the generalization capabilities of TSFDNN. The managerial implications of precisely identifying herded sheep behaviors for production decision-making, agricultural management, animal welfare, and ecological protection are discussed.