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In order to improve the clarity of image fusion and solve the problem that the image fusion effect is affected by the illumination and weather of visible light, a fusion method of infrared and visible images for night-vision context enhancement is proposed. First, a guided filter is used to enhance the details of the visible image. Then, the enhanced visible and infrared images are decomposed by the curvelet transform. The improved sparse representation is used to fuse the low-frequency part, while the high-frequency part is fused with the parametric adaptation pulse-coupled neural networks. Finally, the fusion result is obtained by inverse transformation of the curvelet transform. The experimental results show that the proposed method has good performance in detail processing, edge protection, and source image information.

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In view of the need to remove empty cells and unqualified seedlings for automatic transplanting of leafy vegetable seedlings, this paper proposes a method to detect the growth parameters of leafy vegetable seedlings by using machine vision technology. This method uses the image processor PV200 to perform image grayscale, threshold segmentation, corrosion, expansion, area division, etc. to obtain the pixel value of the leaf area of the seedling and compare it with the set standard value, which provides guiding information for eliminating empty cells and unqualified seedlings. Lettuce seedlings at 17 days, 20 days, and 22 days of seedling age were used as the test objects, and the growth status and test results of the seedlings were analyzed to determine the optimum seedling age for transplanting. The test results show that there is basically no leaf cross-border between the lettuce seedlings at the age of 17 days, the average pixel area of the leaves is 3771.74, and the detection accuracy rate is 100%; the seedlings at the age of 22 days grow 5–6 leaves, the detection accuracy of unqualified seedlings and qualified seedlings was 62.50% and 88.16%, respectively, and the comprehensive detection accuracy was 85.71%. The comprehensive detection accuracy rate showed a downward trend with the increase of seedling age, mainly due to the partial occlusion between leaves. The transplanting of leafy vegetable seedlings is a sparse transplanting operation, and the seedling spacing increases after transplanting. Therefore, the detection of seedlings in the process of transplanting can greatly improve the recognition accuracy and solve the problem that the leaves of the seedlings in the seedling tray are obscured by each other and affect the detection accuracy. The research results can provide a theoretical basis and design reference for the development of the visual inspection system and the transplanting actuator of the leafy vegetable seedlings transplanting robot.

Wireless sensor network technologies have been widely used in modern life especially in Internet of Things. Since battery energy of sensor nodes is limited, balancing the energy consumption of each node on a transmitting path is an important issue. In this article, we propose a multi-hop clustering routing method with fuzzy inference and multi-path tree. The algorithm identifies the best path from the source node to the destination node by following two steps: (1) dividing the wireless sensor nodes by an efficient clustering routing method and (2) determining the optimal path by a combination of the fuzzy inference approach and multi-path method, taking into account the remaining energy, the minimum hops, and the traffic load of node. Simulation results show that the proposed protocol can efficiently reduce the energy consumption of the network, balance network load, and prolong the survival time of the network.

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