In order to meet the needs of rapid detection of field crop growth and guiding variable management, a field crop chlorophyll intelligent detection system based on multi-spectral imaging was designed and developed with maize as an example. It included visible light (RGB) and near-infrared (NIR) image acquisition module, main control processor module, model acceleration module, display and power module, which was used to realize intelligent identification of corn plants and integrated detection of chlorophyll index. Firstly, the canopy image data set of maize seedling stage and jointing stage were collected, and two deep learning models of plant canopy instance segmentation and plant center target detection were compared. A corn plant location detection model based on MobileDet+SSDLite (single shot multibox detector lite) lightweight network was constructed to realize corn plant identification. Secondly, the identified plant heart RGB-NIR images were extracted, the matching and segmentation of RGB and NIR images were carried out, and the gray values of R, G, B and NIR were extracted to calculate the vegetation index. SPXY algorithm (sample set portioning based on joint X-Y distances) and SPA (successive projections algorithm) were used. The samples of the dataset were divided and the characteristic variables were screened, and then GPR (Gaussian process regression) algorithm was selected to establish the chlorophyll index detection model. The results showed that the recognition rate of the model reached 88.7% in the complex environment of occlusion overlap, and the recognition accuracy reached more than 90% in the non-overlapping environment. The model determination coefficient R2 of the modeling set of the chlorophyll content index detection model was 0.62, and the model determination coefficient R2 of the test set was 0.61. Field tests on the developed system showed that the detection rate of the system can reach 14.6 frames per second, and the average accuracy was 92.9%. The research results can effectively solve the problem of corn nutritional status detection in field environment, meeting the real-time detection requirements of field environment, and providing solutions and technical support for intelligent perception of crop production.