Agrivoltaics (AV) offers a promising solution to address both food and energy crises. However, crop growth under photovoltaic (PV) conditions faces substantial challenges due to insufficient light transmission. We propose a large-scale and cost-effective spectral separated concentrated agricultural photovoltaic (SCAPV) system. The system utilizes concentrator modules, cell components, and dual-axis tracking systems to enhance power conversion efficiency (PCE), achieving a maximum PCE of 11.6%. After three years of successful operation, a 10 kWp power plant achieved an average annual electricity generation exceeding 107 MWh/ha. The results showed higher yields of various crops, including ginger and sweet potatoes, and significant improvements in soil moisture retention compared to open air. The improvements in PCE and microclimate validate the scalability of the SCAPV, which provides better plant conditions and cost-effectiveness, with an estimated cost reduction of 18.8% compared to conventional PV power plant. This study provides valuable insights and directions for improvement in AV.

Increasing planting density is crucial for global food security, yet accurately characterizing the 3D spatiotemporal heterogeneity of the canopy microenvironment remains a critical bottleneck. Existing monitoring technologies are hindered by single-point measurements, high costs, and the lack of capabilities for synchronous multi-parameter and multi-layer data acquisition. To address this, this study developed a novel 3D canopy microenvironment measurement instrument integrating 10 adjustable PAR-temperature-relative humidity probes with a 1 Hz maximum sampling frequency. Field calibration demonstrated exceptional accuracy (PAR R² > 0.99, cosine response error < 0.48% at 0°–37.5°) and robust long-term stability, exhibiting only a 0.05% relative error drift over months of continuous high-humidity operation. This instrument was applied to continuously monitor winter wheat under nine planting densities from jointing to maturity. This application enabled the first synchronous characterization of the microenvironment's 3D co-evolution. Key findings revealed that clear-sky conditions produced static light gaps and wind-induced dynamic sunflecks, whereas overcast conditions yielded uniform light transmittance. Furthermore, canopy thermal structures exhibited inversion shifts, with nocturnal cooling surfaces migrating upward during crop development. High-density canopies created effective moisture-retention buffers, while low-density treatments suffered rapid moisture loss during grain-filling. Agronomic analysis highlighted a yield-quality trade-off: increasing density enhanced grain yield but decreased protein and wet gluten contents. Ultimately, this high-spatiotemporal-resolution tool offers broad prospects for crop functional-structural modeling and precision smart agriculture IoT deployment.

Building-integrated photovoltaics (BIPVs) are promising for sustainable urban energy systems but remain challenged by the trade-off among aesthetics, power output, thermal management, and cost. Conventional pigment- or dye-based coloring often reduces power conversion efficiency (PCE) and durability, whereas many structurally colored photovoltaic strategies rely on complex nanostructures that are difficult to scale. Here, we develop a scalable colored photovoltaic approach based on a polymer multilayer film (PMF) fabricated from a PEN/PMMA refractive-index pair by continuous co-extrusion with layer multiplication, enabling vivid structural coloration through selective visible reflection. When integrated with a mid-infrared-emissive encapsulation layer, the PMF simultaneously provides passive radiative cooling, lowering the operating temperature by up to ~8.65 °C, while the demonstrated device retains ~74% of the baseline PCE. To move beyond this representative implementation, we further establish a data-driven opto-thermo-electrical framework that predicts color, efficiency, and operating temperature prior to fabrication across a broad PMF material and structural design space. Large-scale parametric sweeps generate response maps and Pareto frontiers that identify refractive-index combinations best suited to different application scenarios by balancing optical selectivity, electrical performance, and thermal regulation, while a machine-learning-assisted inverse design workflow rapidly maps target colors to feasible PMF structural parameters. This approach provides both a scalable material strategy and a predictive design framework for colored BIPVs with jointly optimized appearance, efficiency, and thermal stability.

Vertical agrivoltaic (V-APV) systems can help enhance the dual productivity of land by integrating food crop cultivation between rows of vertical solar panels. However, conventional V-APV systems suffer from excessive light shading and uneven spatiotemporal distributions of solar radiation. To address these limitations, this study proposes and validates a novel uniform-light vertical agrivoltaic system (U-VPV) that integrates an even-sunlight plate (ESP) to reshape the in-field solar irradiance distribution and microclimate. Field experiments demonstrate that the U-VPV significantly enhances light uniformity and regulates microclimates. The coefficient of variation of morning canopy photosynthetically active radiation is reduced, and the potential evaporation is reduced by approximately 16.5%. Consequently, the system exhibits only a minor fresh weight reduction of 13.2%, showing no significant difference from the unshaded open-field control. Furthermore, the per-module power generation efficiency improved by 10.16% compares to the conventional V-APV system, with the land equivalent ratio (LER) reaching 1.603. By incorporating the commercial and industrial time-of-use electricity pricing mechanism, the comprehensive economic return per unit land area exceeds that of the standard optimal-tilt photovoltaic system by 15.8%. The proposed U-VPV provides a highly feasible engineering pathway for the large-scale deployment and intensive land use of V-APV systems.