Compared to metallic metasurfaces, all-dielectric metasurfaces exhibit significantly lower losses and offer superior sensing performance. However, most existing metasurfaces are limited to single-parameter sensing and exhibit sensitivity to the incident angle of the light source. In this paper, we design and analyze an all-dielectric dual-parameter sensor that leverages structural asymmetry. The sensor consists of an array of silicon elements arranged on a quartz substrate. This metasurface excites two distinct Fano resonance peaks within the near-infrared spectrum, both corresponding to magnetic dipole modes. These resonance peaks demonstrate excellent polarization insensitivity and enhanced stability under oblique incidence. The study demonstrates that this design achieves outstanding performance in biosolution sensing, with a maximum refractive index sensitivity of 404.43 nm/RIU and a maximum temperature sensitivity of 51.76pm/°C.

Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.