Hyperspectral imaging （ HSI ） is an increasingly utilized non-destructive testing technology that simultaneously captures spatial and spectral information of samples, making it suitable for characterizing the spatial distribution of material properties or quickly obtaining the properties of highly heterogeneous samples. However, due to the limitations imposed by sensor and optical material performance and cost, a single hyperspectral camera can only cover a limited spectral range, while the material property information is often distributed across different spectral bands. This limits the types and accuracy of material property monitoring when using a single camera. A push-broom dual-camera HSI system was designed and constructed. The system achieved a minimum spatial resolution of 140.31 μm and 222.72 μm in the spectral ranges of 400~1 000 nm and 1 000~2500 nm, respectively, with spectral resolutions of 2.8 nm and 12 nm, covering a total of464 working bands. A user-friendly data acquisition software, MySpec HSI, was developed by using C# and XAML to facilitate convenient dual-camera HSI data collection. To evaluate the performance of the constructed push-broom dual-camera HSI system, it was used to image the canopy of maize samples, and partial least squares regression models were established for monitoring biomass, chlorophyll, and total nitrogen content in maize canopy leaves. The R values of the biomass, chlorophyll, and total nitrogen content monitoring models based on a visible-near-infrared（VNIR） single camera were 0.567, 0.773, and 0.653, respectively, with RMSEP values of 0.52 g, 2.5, and 0.301%. For the shortwave-infrared（SWIR ）single camera, the R values were 0.566, 0.719, and 0.652, with RMSEP values of 0.53 g, 2.8, and 0.309%. Except for a slight advantage in chlorophyll monitoring by the VNIR band, the monitoring accuracy of the other properties was comparable between the two bands, indicating that either single-camera HSI can achieve biomass, chlorophyll, and nitrogen content monitoring of maize canopy leaves. However, the dual-camera model demonstrated superior performance, with R values for biomass, chlorophyll, and total nitrogen content reaching 0.670, 0.822, and 0.683, respectively, representing improvements of up to 18%, 14%, and 5% compared with that of the single-camera models. The RMSEP values were decreased to 0.46 g, 2.0, and 0.258%, respectively, showing reductions of up to 13%, 27%, and 17% compared with that of the single-camera models, indicating that integrating dual-camera HSI data effectively enhanced the accuracy of monitoring maize canopy leaf properties.