This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management.

The implementation of the “Broadband China” strategy has provided a solid foundation for the construction and development of China’s information network. This paper utilizes panel data from Chinese prefecture-level cities from 2012 to 2020, establishes a multi-period difference-in-differences model to evaluate the policy effects of the “Broadband China” strategy, employs a two-stage method to study its mediating effects, and conducts a heterogeneity analysis. The study finds that the implementation of the “Broadband China” strategy has significantly enhanced urban entrepreneurial vitality. Specifically, the “Broadband China” strategy enhances urban entrepreneurial vitality by improving the level of digital financial inclusion and the application of information technology in cities. The results of the heterogeneity analysis show that cities with lower administrative levels, higher degrees of marketization, and superior business environments exhibit the most significant enhancement in entrepreneurial vitality due to the promotion of the “Broadband China” strategy. Based on these conclusions, this paper argues that the popularization of internet infrastructure can enhance urban entrepreneurial vitality, and proposes relevant policy recommendations for popularizing internet infrastructure in regions with different economic development levels to further boost urban entrepreneurial vitality.

High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural conditions. Ground mobile robots are emerging as the key carrier for resolving this gap because they combine close-range sensing, autonomous mobility, and physical interaction within real field environments. In this paper, a structured scoping review is presented using a closed-loop perception–decision–action pipeline as the organizing principle. Within this framework, recent advances are synthesized from the perspectives of multimodal fusion, localization-aware sensing, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. The review further clarifies the complementary roles of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and air–ground collaboration in multiscale phenotyping workflows. Beyond summarizing technologies, the article provides three concrete deliverables: a structured taxonomy of mobile phenotyping systems; comparative tables covering sensing modalities, localization/navigation methods, and AI models; and a research agenda linking technical progress to field deployability. The synthesis highlights four persistent bottlenecks, namely environmental generalization, annotation scarcity, limited standardization and reproducibility, and the gap between advanced models and agricultural edge hardware. Overall, ground robots are identified not merely as sensing platforms, but as the central system architecture for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable operation.

Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation $R^2$ = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza.

High-throughput plant phenotyping is increasingly constrained by the mismatch between the demand for field-relevant, fine-grained phenotypic data and the limited capability of conventional observation platforms under complex agricultural conditions. In this context, mobile phenotyping systems, particularly ground robots, are emerging as a key technological pathway for bridging macro-scale monitoring and organ-level trait analysis. This review examines the development of mobile phenotyping platforms for high-throughput plant phenotyping, with emphasis on the evolving role of ground robots in field-based sensing, decision-making, and active interaction. We first compare the functional characteristics of unmanned aerial vehicles and unmanned ground vehicles and discuss their complementarity in multiscale phenotypic data acquisition. We then summarize recent advances in the core technical framework of mobile phenotyping robots, including multimodal perception, localization and mapping, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. Major challenges are further discussed, particularly those related to environmental generalization, data annotation, standardization, reproducibility, and long-term field reliability. Finally, future directions are outlined from the perspectives of air–ground collaboration, multi-robot systems, foundation models, and embodied intelligence. This review highlights ground robots as a central carrier for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable systems.

In the study of consumer behavior, we believe that a distinction should be made between the subjective mental activity of consumption and the objective process of consumption experience, and that the deviation and fluctuation of “decision utility” and the randomness of “experience utility” have essential effects on consumer behavior. For one thing, purchase often precedes experience, and consumers cannot precisely predict utility and its distribution but can only make decisions based on adaptive expectations. Secondly, there is often uncertainty in the experience of goods, making predictions more difficult. Thirdly, the revision of consumer decision utility is carried out through memory in an adaptive process. We introduce two stochastic forms of decision and experience utility functions and build a two-period model. In the model, consumers make decisions based on a pre-determined decision utility function in the first period and a randomly realized experience utility function in the second period. The analysis shows that the volatility of “decision utility” and “experience utility” affects consumers in opposite directions; the former may trigger expansionary consumption, while the latter makes consumers more cautious. Finally, the consumption behaviors in the model can be divided into 24 categories based on the dimensions of chance, systematicity, luck, and deviance, corresponding to various scenarios. The total number of consumer behavior categories is a full ranking of the size relationship of the four factors mentioned above, thus 24 categories. For example, when good luck is accompanied by chance underestimation versus systematic underestimation, it leads to a better process experience for the consumer.