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Based on the sensitive characteristics of chlorophyll molecules to light absorption and reflection in the visible and near-infrared spectral range （400~1 000 nm）, a crop chlorophyll detector based on a contact image sensor can be designed to achieve non-destructive, rapid, and accurate detection of crop chlorophyll content. Firstly, a hyperspectral camera was used to collect the reflection spectrum of cornleaves in the range of 397～1003 nm, and the true value of leaf chlorophyll content was extracted by using spectrophotometry. Nextly, the screening of chlorophyll - sensitive response wavelengths was carried out. The Monte Carlo uninformative variable elimination（MC - UVE）algorithm was used to screen variables within the range of 10 to 50 feature wavelengths, and it was found that using 30 feature wavelengths provided the optimal detection capability for chlorophyll content. Simultaneously, the successive projections algorithm（SPA）was employed for feature wavelength screening. The two algorithms yielded a total of seven overlapping feature wavelengths. Further, through correlation analysis between the bands and chlorophyll content, low-correlation band was eliminated, ultimately resulting in six feature wavelengths. The selected feature wavelengths were used to choose the bands for the contact image sensor. The hardware of the device mainly included sensor image acquisition, main controller, display, and other modules, which realized the functions of near - infrared and visible light reflection spectrum data acquisition, processing, display, and storage of crop leaves. Sensor performance tests and field application tests were conducted. By analyzing the reflectivity of the obtained multispectral images, a partial least squares detection model for chlorophyll content was constructed，with a coefficient of determination for the validation set of 0.697. By analyzing the correlation between various vegetation indices and chlorophyll content, the normalized difference red edge（NDRE）, green minus red（GMR）, and normalized difference red edge（MTCI）vegetation indices with higher correlation were selected for further combined modeling, improving the detection model accuracy to 0.706. The model was embedded into the system, ultimately achieving rapid detection of chlorophyll content in the field and providing technical support for crop growth analysis.