Smart agriculture is rapidly evolving in response to growing global demands for food security and sustainable resource management. Cloud–edge–device collaborative computing has emerged as a transformative paradigm, addressing the limitations of traditional centralized architectures by enabling distributed intelligence, real-time processing, and adaptive decision-making. This review provides a comprehensive overview of the architectures, technical characteristics, and application scenarios of cloud–edge–device collaboration in agriculture. Key domains covered include environmental monitoring, intelligent irrigation, UAV–machinery coordination, livestock health management, and pest and disease control. Major challenges such as device heterogeneity, data consistency, resource constraints, and privacy concerns are identified and discussed. Furthermore, six critical research directions are outlined, including intelligent scheduling algorithms, lightweight edge AI, hierarchical data fusion, federated learning, interoperability frameworks, and digital twin technologies. This review aims to serve as a practical reference and theoretical foundation for advancing the design and implementation of next-generation smart agriculture systems.

Accurate parameter estimation is vital in instrumentation and measurement, especially for modeling dynamic systems based on measurement data. Recurrent networks have been widely adopted for this purpose, with the output-input feedback (OIF) Elman network offering enhanced modeling through its dual-feedback structure. However, most existing parameter estimation algorithms for such a network underutilize historical measurements and exhibit limited adaptability to its dual-feedback mechanism. This paper proposes a dynamic factor and multi-innovation-based gradient parameter estimation algorithm for the OIF Elman network. The algorithm is derived from a multi-innovation cost function augmented with a weight decay factor, enabling dynamic balancing between current and past measurements to improve estimation accuracy. A dynamic factor adjustment strategy is introduced to adaptively tune both the weight decay factor and the self-feedback factors of the network based on the evolving weight parameter estimates. Unlike batch-based methods, the proposed algorithm supports recursive updates, enabling real-time parameter estimation from sequential measurement data. Convergence and computational complexity analyses confirm the theoretical properties of the proposed algorithm, while numerical simulations and a real-world electric load forecasting case study demonstrate its superior modeling performance compared to existing methods.

Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods.

As an effective way of improving traffic efficiency, vehicle platoon control has attracted extensive interest recently. Communication between vehicles tends to be affected by communication noises. Aimed at improving communication efficiency, an event-triggered vehicle platoon control under random communication noises is studied in this paper. First, for vehicle platoons with linear third-order dynamics, a time-varying consensus gain $c(t)$ is introduced to reduce the effects of the communication noises. Second, with the introduction of the algebraic graph theory and matrix analysis theory, conditions for internal stability and $l_{p}$ -string stability under random additive communication noises are derived. Third, by utilizing the Lyapunov approach and $It\hat {o}$ stochastic differential equations, the consensus of vehicle platoon under random additive communication noises is proved. Last, to reduce the frequent communication between vehicles, an event-triggered mechanism is introduced, and the design for the triggering parameter is derived. The effectiveness of the proposed method is verified with some numerical simulations.