Solar Insecticide Lamp (SIL) is a green prevention and control technology for pests. As the development of Wireless Sensor Networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things -Solar Insecticide Lamp Internet of Things (SIL-IoT). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this paper, we study the SIL Deployment Problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary and partition structure. According to partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. Experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

Solar insecticidal lamps (SILs) play a vital role in green prevention and control of pests. By embedding SILs in wireless sensor networks (WSNs), we establish a novel agricultural Internet of Things (IoT), referred to as the SIL-IoTs. In practice, the deployment of SIL nodes is determined by the geographical characteristics of an actual farmland, the constraints on the locations of SIL nodes, and the radio-wave propagation in a complex agricultural environment. In this article, we mainly focus on the constrained SIL deployment problem (cSILDP) in a mixed-crop farmland, where the locations used to deploy SIL nodes are a limited set of candidates located on the ridges. We formulate the cSILDP in this Scenario as a connected set cover (CSC) problem and propose a hole-aware node deployment method (HANDM) based on the greedy algorithm to solve the constrained optimization problem. The HANDM is a two-phase method. In the first phase, a novel deployment strategy is utilized to guarantee only a single coverage hole in each iteration, based on which a set of suboptimal locations is found for the deployment of SIL nodes. In the second phase, according to the operations of deletion and fusion, the optimal locations are obtained to meet the requirements on complete coverage and connectivity. Experimental results show that our proposed method achieves better performance than the peer algorithms, specifically in terms of deployment cost.

Farmland boundary information plays a key role in agricultural remote sensing, and it is of importance to modern agriculture. We collected the relevant research in this field at home and abroad in this review, and we systematically assessed the farmland boundary extraction process, detection algorithms, and influencing factors. In this paper, we first discuss the five parts of the assessment: (1) image acquisition; (2) preprocessing; (3) detection algorithms; (4) postprocessing; (5) the evaluation of the boundary information extraction process. Second, we discuss recognition algorithms. Third, we discuss various detection algorithms. The detection algorithms can be divided into four types: (1) low-level feature extraction algorithms, which only consider the boundary features; (2) high-level feature extraction algorithms, which consider boundary information and other image information simultaneously; (3) visual hierarchy extraction algorithms, which simulate biological vision systems; (4) boundary object extraction algorithms, which recognize boundary object extraction ideas. We can subdivide each type of algorithm into several algorithm subclasses. Fourth, we discuss the technical factors and natural factors that affect boundary extraction. Finally, we summarize the development history of this field, and we analyze the problems that exist, such as the lack of algorithms that can be adapted to higher-resolution images, the lack of algorithms with good practical ability, and the lack of a unified and effective evaluation index system.

Federated Learning is a promising technique that facilitates cloud–edge–terminal collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without centralizing data, addressing privacy, and security concerns. However, when applied to AIoT, this technique faces several challenges, such as low communication efficiency among terminal devices, edges, and cloud platforms. In this article, we propose a novel approach called asynchronous federated learning with quantization (QuAsyncFL), which combines asynchronous federated learning with an unbiased nonuniform quantizer to address the issue of low communication efficiency. Moreover, we provide a detailed theoretical analysis of convergence with quantized gradients proving that the model could converge to a certain bound. Our experiments demonstrate that QuAsyncFL outperforms the original approach, achieving significant improvements in terms of communication efficiency. The research results represent a further step toward developing cloud–edge–terminal collaboration enabled AIoT.

As an important physical control technology, solar insecticidal lamp (SIL) can effectively prevent and control the occurrence of pests. The combination of SILs and wireless sensor networks (WSNs) initiates a novel agricultural Internet of Things (IoT), i.e., SIL-IoTs, to simultaneously kill pests and transmit pest information. In this article, we study the weighted SIL deployment problem (wSILDP) in SIL-IoTs, where weighted locations on ridges are prespecified and some of them are selected to deploy SILs. Different from the existing studies whose optimization objective is to minimize the deployment cost, we consider the deployment cost and the total weight of selected locations jointly. We formulate the wSILDP as the weighted set cover (WSC) problem and propose a layered deployment method based on greedy algorithm (LDMGA) to solve the defined optimization problem. The LDMGA is composed of two phases. First, SILs are deployed layer by layer from the boundary to the center until the entire farmland is completely covered. Second, on the basis of three design operations, i.e., substitution, deletion and fusion, the suboptimal locations obtained in the first phase are fine-tuned to achieve the minimum deployment cost together with the maximum total weight for meeting the coverage and connectivity requirements. Simulation results clearly demonstrate that the proposed method outperforms three peer algorithms in terms of deployment cost and total weight.

Carrying out comprehensive green prevention and control on pests and plant diseases is important for improving quality of agricultural products, reducing usage of agricultural pesticides, and controlling losses. As an important approach of physical control, Solar Insecticidal Lamp has the counting function by killing pests during its working period, further obtaining the information of pest population density in the area covered by the SIL. Motivated by the current voltage recognition and counting technology, which cannot cope with the situation of killing a pest by continuous discharge, thus cannot provide an accurate insecticidal quantity, we proposed a novel accurate insecticidal counting method from the aspect of hardware and software. In the aspect of hardware, the SIL testbed is modified by changing high voltage package, and adding voltage comparator module and sound sensor module, further collecting the data Pulse Number of Insecticidal Discharges and Pulse Number of Insecticidal Sounds. In the aspect of software, we experimentally validated the relationship between PNID and PNIS as well as insecticidal quantity by seven different machine learning algorithms. Finally, we provide compelling experimental results and discuss strong evidence that points to a future need for lightweight edge computing on SIL, as a valuable consumer electronics.