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PurposeIntegrality of surface mesh is requisite for computational engineering. Nonwatertight meshes with holes can bring inconvenience to applications. Unlike simple modeling or visualization, the downstream industrial application scenarios put forward higher requirements for hole-filling, although many related algorithms have been developed. This study aims at the hole-filling issue in industrial application scenarios.Design/methodology/approachThis algorithm overcomes some inherent weakness of general methods and generates a high-level resulting mesh. Initially, the primitive hole boundary is filled with a more appropriate triangulation which introduces fewer geometric errors. And in order for better performances on shape approximation of the background mesh, the algorithm also refines the initial triangulation with topology optimization. When obtaining the background mesh defining the geometry and size field, spheres on it are packed to determine the vertex configuration and then the resulting high-level mesh is generated.FindingsThrough emphasizing geometry recovery and mesh quality, the proposed algorithm works well in hole-filling in industrial application scenarios. Many experimental results demonstrate the reliability and the performance of the algorithm. And the processed meshes are capable of being used for industrial simulation computations directly.Originality/valueThis paper makes input meshes more adaptable for solving programs through local modifications on meshes and perfects the preprocessing technology of finite element analysis (FEA).

With the extensive use of the Internet of Things (IoT) in agriculture, the number of terminals are also grow rapidly. This will increase the network traffic and computing pressure of the centralized server. The centralized data processing mode used in traditional agriculture cannot meet the needs of the Internet of everything era. This paper designs a gateway based on edge-computing technology for monitoring crop growth environment. It uses virtualized container technology to package long-range wide-area network (LoRaWAN) server, pest identification, and environmental information data fusion functions into images. It forms integrated operation mode of multiple function in agriculture. The gateway applies message-oriented middleware to standardize and customize the data transmission among functional modules, clouds, and edges. Through simulation and field test, the designed gateway can achieve the functions of each module at the same time, the resource utilization, and the transmission quality are stable. The edge-computing gateway has the advantages of low cost, low latency, and low power consumption which has practical significance.

The Internet of Things (IoT) technology focuses on the application of information-sensing equipment, transmission network, and intelligent information processing technology. Through scientific management, it can achieve the rational use of agricultural resources, thus improving the ecological environment, reducing production costs, and improving the yield and quality of agricultural products. At present, the application of agricultural IoT technology faces problems including small network coverage, limited monitoring projects, high transmission cost, and short power supply time, which limit the popularity and ability of farmland environmental monitoring. In this paper, ns-3 is used to simulate the IoT for farmland environmental monitoring based on the long-range wide-area network (LoRaWAN) protocol. The simulation results show the transmission distance with good transmission quality under the LoRaWAN protocol. Furthermore, we analyze the comparison of the combination range of network transmission quality parameters under the two acknowledgment (ACK) mechanisms. Finally, we present the parameter combination range and suggestions to improve the network transmission quality, which provide a new basis for guiding the farmland environmental monitoring system.

This article applies deep learning and electromechanical technology to plant phenotype measurement. First, an electromechanical device is designed to collect plant phenotype images, which solves the difficulty of collecting deep learning training data. The data set required for deep learning model training for plant phenotype detection is made by an automated method. This paper takes the Lactuca sativa plant image as an example and uses the ASM-based data enhancement method to solve the problem of insufficient image data of Lactuca sativa leaf pests and effectively avoid the phenomenon of overfitting. The plant image recognition method based on deep learning proposed breaks through the limitations of plant local feature recognition, gets rid of the limitation of highly specialized data collection, lowers the threshold of plant image recognition, and has advantages in recognition speed and accuracy. This method requires a large amount of training data. In the future, we can explore the collection of massive plant pictures from the Internet as a training set to achieve rapid iteration and optimization of the model.