In order to quickly analyze the growth of the crop in the field, the spectral imaging sensor was used to detect the chlorophyll content of the maize canopy. The images of 47 maize plants were photographed using an IMEC 5×5 imaging unit multispectral camera. The camera was designed based on the coating principle to obtain spectral images of 25 wavelengths in the range of 673～951 nm. At the same time, the chlorophyll content was measured by SPAD-520 device. There were 2～3 sampling points in each leaf, and they were measured 3 times at each point, so that 251 sample data were collected. The multi-spectral images were processed. Firstly, the gray pixel values of the same band in the imaging unit were extracted based on the principle of the coating spectral imaging sensor. The extraction methods included image splitting and recombination, in which 25 images were extracted from the original image. Secondly, a linear inversion formula between the gray value of multi-spectral images and the gray plate standard reflectance was established. The gray plate standard was made up of 4 gray level standard plates. Thirdly, the image segmentation algorithm was established to reduce the background influence in maize canopy images, in which the OTSU algorithm was used to eliminate the interference of the soil and the Hough circle transform algorithm was used to eliminate the interference of the flowerpot. Lastly, the study used the Mahala Nobis distance algorithm to eliminate abnormal sample data. According to the proportion of 2∶1, the total samples were divided into calibration set (170 samples) and validation set (73 samples) by SPXY (Sample set partitioning based on joint X-Y distance) algorithm. The partial least squares regression (PLSR) model was established to detect the chlorophyll content of the maize canopy. In general, the results of spectral bands reflectance linear calibration fitting model were above 0.99. The corrected data was consistent with the ASD spectral reflectance before calibration. The interference of soil and flowerpot background noise in the multi-spectral images were removed by the image segmentation algorithm. The calibration accuracy of PLSR model was 0.545 1, and the validation accuracy was 0.472 6. And then the chlorophyll content of each pixel in the maize canopy images could be calculated by the PLSR modeling result. As a result, the chlorophyll content distribution could be visually analyzed and indicated the growth status. The study could provide technical and application support for the chlorophyll distribution of field maize plants.