In recent years, the application of artificial intelligence (AI) in agriculture has achieved remarkable progress. As an advanced AI technology, Agricultural Large Models exhibit powerful capabilities in multimodal information fusion, complex reasoning, and decision-making. Meanwhile, 5G networks-with their high bandwidth, low latency, and massive connectivity-have significantly enhanced agricultural data acquisition capabilities, enriched large-scale model datasets, and facilitated the deep integration of these models into agricultural digital transformation. This paper proposes a three-tier collaborative architecture for Agricultural Large Models and validates it in 5G-enabled precision planting scenarios. Experimental results demonstrate that the proposed framework effectively improves the efficiency and effectiveness of precision planting, particularly in disaster early warning and resource management applications.

Enterprises loss is a growth strategy, in which enterprises migrate across regions/cities to adapt to the changes of internal and external environment, in this way to seek new development space and further reach the growth again. As the carrier of local economic development, the transfer of enterprises from one region to another undoubtedly means the loss of regional resources for the region. This paper takes large- scale enterprises as the research object. Then, this paper uses questionnaire data and statistical data, and adopts the combination of PCA algorithm and extreme value standardization method to comprehensively evaluate the loss probability of enterprises. This method will reflect the loss tendency of enterprises in the region, and make an empirical analysis on the large-scale enterprises in region, in this way to help regional managers have an early insight into the loss tendency of enterprises in the region. Finally, it will provide a reference for stabilizing the regional economy and help reduce the loss risk of large-scale enterprises in the region.

Based on the development status of telecom operators in the smart home business, and combined with the digital China strategy, this paper proposes a research idea and a technical solution for building a digital main center platform for smart home based on big data. This solution can realize a more convenient and easy-to-use big data productivity platform, provide a more comprehensive data index system, data processing capabilities, and data integration standards, and provide guidance for building a customizable and reliable data product system. This platform can enhance the powerful supporting force of the data economy under the new development pattern of the communications industry and promote the intelligent level of smart home business.

Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.