Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture.

Time synchronization is one of the key technologies in Industrial Wireless Sensor Networks (IWSNs), and clustering is widely used in WSNs for data fusion and information collection to reduce redundant data and communication overhead. Considering IWSNs’ demand for low energy consumption, fast convergence, and robustness, this paper presents a novel Cluster-based Maximum consensus Time Synchronization (CMTS) method. It consists of two parts: intra-cluster time synchronization and inter-cluster time synchronization. Based on the theory of distributed consensus, the proposed method utilizes the maximum consensus approach to realize the intra-cluster time synchronization, and adjacent clusters exchange the time messages via overlapping nodes to synchronize with each other. A Revised-CMTS is further proposed to counteract the impact of bounded communication delays between two connected nodes, because the traditional stochastic models of the communication delays would distort in a dynamic environment. The simulation results show that our method reduces the communication overhead and improves the convergence rate in comparison to existing works, as well as adapting to the uncertain bounded communication delays.

Time synchronization is critical for wireless sensors networks in industrial automation, e.g., event detection and process control of industrial plants and equipment need a common time reference. However, cyber-physical attacks are enormous threats causing synchronization protocols to fail. This paper studies the algorithm design and analysis in secure time synchronization for resource-constrained industrial wireless sensor networks under Sybil attacks, which cannot be well addressed by existing methods. A node-identification-based secure time synchronization (NiSTS) protocol is proposed. The main idea of this protocol is to utilize the timestamp correlation among different nodes and the uniqueness of a node’s clock skew to detect invalid information rather than isolating suspicious nodes. In the detection process, each node takes the relative skew with respect to its public neighbor as the basis to determine whether the information is reliable and to filter invalid information. The information filtering mechanism renders NiSTS resistant to Sybil attacks and message manipulation attacks. As a completely distributed protocol, NiSTS is not sensitive to the number of Sybil attackers. Extensive simulations were conducted to demonstrate the efficiency of NiSTS and compare it with existing protocols.

The bearing fault diagnosis based on deep learning algorithms requires a substantial amount of data. However, in practical industrial production, the diagnostic algorithms tend to work ineffectively due to the limitations of samples. Therefore, in this article, we propose an improved method of deep convolutional generative adversarial networks (DCGAN) with discriminator gradient gap regularization (IDIG-GAN), which can effectively solve the problems of unstable training and poor training performance under a small sample dataset. Firstly, the self-attention mechanism is integrated into the DCGAN to capture global information to enhance the generalization capability of the network. Moreover, gradient normalization is applied to the discriminator to address the problem of vanishing gradients in the network. Furthermore, gradient gap regularization is incorporated into the loss function to narrow the gap between the discriminator gradient norms, thereby improving network stability when dealing with small fault datasets. Through training with the improved IDIG-GAN, then the generated samples are used to expand the dataset and construct a fault diagnosis model. By verifying under two bearing datasets, the results demonstrate that the proposed method can generate high-quality samples and effectively enhance the diagnostic capability of the network when working with small datasets.

Accurate time synchronization is essential for data fusion, deterministic scheduling, and other fundamental operations in industrial wireless sensor networks. The relative clock skew plays a key role in popular distributed time synchronization and directly affects the estimation accuracy of clock compensations. However, communication delays are inevitable and unpredictable in practical scenarios, which makes an error calculation of relative clock skew for most synchronization protocols. This paper investigates the time synchronization problem with Gaussian distribution delays. A maximum likelihood estimator (MLE) of the relative clock skew is derived, and its Cramer-Rao lower bound is given. Without modifying the synchronization protocol and packet format, MLE gathers the observed values in past several synchronization periods to accurately estimate the relative clock skew of current round. The proposed MLE is independent of clock skew and clock offset estimations. It can be applied to different distributed protocols. Extensive simulation results indicate that the convergence accuracy of the proposed MLE-based synchronization protocols is better than that with the ATS, GTSP, and MTS protocols. In addition, the accuracy of the estimator depends on the synchronization period and the window length of MLE, which provides flexible accuracy levels for various industrial applications.

Security is an important factor that cannot be neglected in the design of time synchronization algorithms since industrial wireless sensor networks are prone to attacks against physical nodes and communication links. The Sybil attack is an intelligent attack with a high destructive capacity in pretending multiple identities and broadcasting illegitimate messages to destroy the network operation. Existing secure time synchronization algorithms mostly focus on distributed protocols; however, they pay less attention to Sybil attacks and centralized network time synchronization. In this paper, we propose a novel reference broadcast-based secure time synchronization (RSTS) for industrial wireless sensor networks with a time source against Sybil attacks. Different from previous protocols, in converging the network structure and the clock status, RSTS employs a public neighbor forwarding mechanism based on reference broadcast to filter the illegal time information automatically. Instead of establishing a table with timestamps of packet transmission and receipt, the least square linear regression is utilized to estimate the compensation relative to the source node with the recorded time and calculated time difference in receiving packets. The simulation results demonstrate that RSTS is resilient to Sybil attacks as well as message manipulation attacks in comparison with existing algorithms.