Maize (Zea mays L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements.

Grassroots community governance has gained increasing attention for its vital role in resource integration and multi-actor collaboration. As an innovative governance model, the “Five-Sector Linkage” (FSL) mechanism enhances service efficiency by aligning the efforts of communities, social organizations, social workers, volunteers, and philanthropic actors. However, quantitative research on interaction dynamics within such mechanisms remains insufficient, particularly regarding the optimization of collaborative networks for improved governance outcomes. This study applies Social Network Analysis (SNA) to the “After-School Program” project in Community B, Chengdu, to examine the structural features and interrelations of multi-actor cooperation under the FSL framework. The collaboration network consists of 39 nodes and 1482 links, with a density of 0.370 and an average path length of 1.632, indicating efficient communication and moderate cohesion. Degree and betweenness centrality analyses identify social workers (C1–C3) as key hubs, with C2 holding the highest bridging role (B_C = 81.401). The overall network shows low centralization (4.19%) and limited heterogeneity (2.74%), reflecting a polycentric and resilient structure. Inter-sectoral analysis showed that all nodes interacted with at least one social worker, while community actors (A1, A2) engaged broadly across 18 nodes. Volunteers maintained extensive grassroots connections, while philanthropic resources formed selective but strategic links with 13 nodes. These findings provide empirical insights into the coordination logic of the FSL mechanism and offer guidance for building adaptive, decentralized community governance networks. Future research should explore longitudinal dynamics and cross-community comparisons to further enhance the applicability of the model.

Uniform legal requirements for written farmland-rental contracts do not necessarily produce uniform contracting practices. This study examines how policy-tool design, transaction heterogeneity, and institutional access jointly structure written-contract adoption in rural China. We coded 101 provincial policy documents across 27 provincial-level regions, of which 69 documents contributed to the baseline policy stock as of 31 December 2021, and analyzed 7237 farmland-transfer transactions using fixed-effects models, common-support restrictions, overlap weighting, and entropy balancing. General contract norms were more extensively operationalized than differentiated risk-governance tools (mean scores, 1.673 vs. 1.309), a pattern observed in 19 of 27 regions. Written-contract adoption was higher among professional agricultural operators and in larger transactions, whereas the positive unadjusted difference for non-local operators reversed after covariate adjustment. Transactions reporting a village-level organization or intermediary channel had an adjusted adoption rate 16.05 percentage points higher than other transactions; estimates remained positive under more local fixed effects and weighting, although magnitude and precision varied across channel configurations and target populations. These findings indicate that contractual formalization is associated with both transaction-specific demand for safeguards and practical access to formal institutions. Effective governance should therefore combine accessible general contracting procedures with tiered risk safeguards and organizational services, while avoiding causal claims from the observational estimates.