This paper presents an agricultural management service system that aims to meet the needs of Internet of Things (IoT) information upgrades in China’s family farms. The proposed agricultural management service system consists of Near Field Communication (NFC) tags, in-field service nodes, and smartphones. NFC tags are used as the core identifier of various agricultural management elements. The in-field service node, which is based on a programmable system-on-chip with intellectual property cores (IP core), supports distributed agriculture device management and smartphone operations. Smartphones in the proposed system include the management assistant application (app) and management service app, which are designed for agricultural management support functions and agricultural management application requirements. Through this system, the needs of diverse agricultural management practices can be effectively satisfied by a unified system structure. The practical results show that the design can be used to construct diversified agricultural IoT information application service systems simply and effectively, and it is especially suitable for Chinese family farm operators who are implementing IoT information upgrades for smart agriculture.

Modern city construction focuses on developing smart transportation, but the recognition of the large number of non-motorized vehicles in the city is still not sufficient. Compared to fixed recognition equipment, drones have advantages in image acquisition due to their flexibility and maneuverability. With the dataset collected from aerial images taken by drones, this study proposed a novel lightweight architecture for small objection detection based on YOLO framework, named EBR-YOLO. Firstly, since the targets in the application scenario are generally small, the number of Backbone layers is reduced, and the AZML module is proposed to enrich the detail information and enhance the model learning capability. Secondly, the C2f module is reconstructed using part of the convolutional PConv to reduce the network’s computational volume and improve the detection speed. Finally, the downsampling operation is reshaped by combining with the introduced ADown module to further reduce the computational amount of the model. The experimental results show that the algorithm achieves an mAP of 98.9% and an FPS of 89.8 on the self-built dataset of this paper, which is only 0.2% and 0.3 lower compared to the original YOLOv8 network, respectively, and the number of parameters is 70% lower compared to the baseline, which ensures the accuracy and computational speed of the model while reducing its computational volume greatly. At the same time, the model generalization experiments are carried out on the UCAS-AOD and CARPK datasets, and the performance of the model is almost the same as the baseline.

Student–teacher networks have shown promise in unsupervised anomaly detection; however, issues such as semantic confusion and abnormal deformations still restrict the detection accuracy. To address these issues, we propose a novel student–teacher network named MST by integrating the multistage pixel-reserving bridge (MPRB) and the spatial compression autoencoder (SCA) to the MMR network. The MPRB enhances inter-level information interaction and local feature extraction, improving the anomaly localization and reducing the false detection area. The SCA bolsters global feature extraction, making the detection boundaries of larger defects clearer. By testing our network across various datasets, our method achieves state-of-the-art (SOTA) performance on AeBAD-S, AeBAD-V, and MPDD datasets, with image-level AUROC scores of 87.5%, 78.5%, and 96.5%, respectively. Furthermore, our method also exhibits competitive performance on the widely utilized MVTec AD dataset.