In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies.

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.