Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field.

In recent years, with the frequent crises of trust in the food industry, food safety issues have become an issue of particular concern to consumers. The traditional agricultural food logistics and transportation model can no longer guarantee the traceability of food safety information, and it is particularly important to establish an effective traceability system. Therefore, a traceability system is applied as a tool to assist in the assurance of food safety and quality, as well as to achieve consumer confidence. Blockchain is a new decentralized infrastructure and distributed computing paradigm that has gradually emerged. Blockchain has the characteristics of decentralization, immutable information and trustworthiness. When blockchain is applied to the traceability system of agricultural products, the quality of agricultural products will be well guaranteed. This article aims to provide a comprehensive review of the recent research efforts on traceability in agricultural products based on blockchain technology. First, the method of content analysis used in this article to sort out the papers is introduced. Second, the background concepts of blockchain and the key technologies are presented. Thirdly, the traceability applications based on blockchain technology for agricultural products are described in detail. Finally, we expand on the current trends and provide new perspectives pertaining to this exciting application of this field.

Vehicular Ad Hoc Networks (VANETs) employ multichannel to provide a variety of safety and non-safety applications, based on the IEEE 802.11p and IEEE 1609.4 protocols. The safety applications require timely and reliable transmissions, while the non-safety applications require efficient and high throughput. In the IEEE 1609.4 protocol, operating interval is divided into alternating Control Channel (CCH) interval and Service Channel (SCH) interval with an identical length. During the CCH interval, nodes transmit safety-related messages and control messages, and Enhanced Distributed Channel Access (EDCA) mechanism is employed to allow four Access Categories (ACs) within a station with different priorities according to their criticality for the vehicle’s safety. During the SCH interval, the non-safety massages are transmitted. An analytical model is proposed in this paper to evaluate performance, reliability and efficiency of the IEEE 802.11p and IEEE 1609.4 protocols. The proposed model improves the existing work by taking serval aspects and the character of multichannel switching into design consideration. Extensive performance evaluations based on analysis and simulation help to validate the accuracy of the proposed model and analyze the capabilities and limitations of the IEEE 802.11p and IEEE 1609.4 protocols, and enhancement suggestions are given.

Vehicular Ad Hoc Networks (VANETs) employ multichannel to provide a variety of safety and non-safety (transport efficiency and infotainment) applications, based on the IEEE 802.11p and IEEE 1609.4 protocols. Different types of applications require different levels Quality-of-Service (QoS) support. Recently, transport efficiency and infotainment applications (e.g., electronic map download and Internet access) have received more and more attention, and this kind of applications is expected to become a big market driver in a near future. In this paper, we propose an Efficient and QoS supported Multichannel Medium Access Control (EQM-MAC) protocol for VANETs in a highway environment. The EQM-MAC protocol utilizes the service channel resources for non-safety message transmissions during the whole synchronization interval, and it dynamically adjusts minimum contention window size for different non-safety services according to the traffic conditions. Theoretical model analysis and extensive simulation results show that the EQM-MAC protocol can support QoS services, while ensuring the high saturation throughput and low transmission delay for non-safety applications.

With the advancement of modern agriculture, intelligent laser robots driven by deep learning have emerged as an effective solution to address the limitations of traditional weeding methods. These robots offer precise and efficient weed control, crucial for boosting agricultural productivity. This paper provides a comprehensive review of recent research on laser weeding applications using intelligent robots. Firstly, we introduce the content analysis method employed to organize the reviewed literature. Subsequently, we present the workflow of weeding systems, emphasizing key technologies such as the perception, decision-making, and execution layers. A detailed discussion follows on the application of deep learning algorithms, including Convolutional Neural Networks (CNNs), YOLO, and Faster R-CNN, in weed control. Here, we show that these algorithms can achieve high accuracy in weed detection, with YOLO demonstrating particularly fast and accurate performance. Furthermore, we analyze the challenges and open problems associated with deep learning detection systems and explore future trends in this research field. By summarizing the role of intelligent laser robots powered by deep learning, we aim to provide insights for researchers and practitioners in agriculture, fostering further innovation and development in this promising area.

With the ever-growing volume of online information, recommender system has been an effective strategy to overcome such information overload. Recommender systems are widely used in many web applications, such as e-commerce, news, agriculture and other fields. Agricultural informatization is an important driving force for the development of agricultural modernization. With the further improvement of agricultural informatization infrastructure construction, the use of modern information technology to achieve personalized agricultural information resource recommendation services and provide users with timely and effective information has become an effective solution. This article aims to provide a comprehensive review of recent research efforts on application of agricultural information based on intelligent recommender systems. Firstly, the method of content analysis used in this article to sort out the papers is introduced. Secondly, the background concepts of recommender systems and the key technologies are presented. Thirdly, the applications of recommender systems/technologies for agricultural information are described in detail. Finally, a summary and outlook on the application of recommender systems for agricultural information are provided.