Timely detection of chlorophyll distribution within crop canopies is crucial due to its non-uniform and dynamic characteristic. Close-range hyperspectral imaging (HSI) shows great potential for rapid quantifying the chlorophyll distribution on detached leaves, but the canopy HSI presents greater challenges due to the complex interaction of illumination with canopy geometry. The spectral pre-processing techniques have shown effectiveness in mitigating some negative effects; however, there has been lacking the comprehensive analysis and effective mitigation of illumination influences on close-range HSI of relatively complex canopies. In this study, the basil crops cultivated under different light intensities were taken as the object. Firstly, the chlorophyll, growth, and spectral responses of basil crops to varying light intensities were examined. Then, through a comprehensive analysis of the illumination influences on canopy close-range HSI, several spectral pre-processing methods were selected. Subsequently, by evaluating regression modeling performances and canopy chlorophyll distribution maps, the combination of Savitzky-Golay-standard normal variate (SG-SNV) spectral pre-processing method and random forest (RF) model was identified as the optimal close-range HSI analysis pipeline, which enabled the effective mitigation of illumination influences. Its application to hyperspectral images of basil canopies cultivated under different light intensities yielded satisfactory chlorophyll distribution maps, consistent with observed differences in chlorophyll levels. Furthermore, the reason behind the proposed analysis pipeline in mitigating illumination influences in complex canopies was discussed, that the spectral pre-processing method and appropriate nonlinear model were effective in reducing the linear (eg. working distance, surface angle) and nonlinear (eg. multiple scattering) effects, respectively. The overall results demonstrated that, based on the analysis of illumination influences, the proposed close-range HSI pipeline permitted the mitigation of these influences substantially, enabling the in situ detection of canopy chlorophyll distribution. This methodology holds significant importance for timely monitoring chlorophyll status of whole canopies, thereby enhancing the planting management strategies.