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A rapid and accurate detection of a maize plant has been widely determined the food safety and quality assurance in modern agriculture. However, there is a great challenge on the complex environment of canopy leaves at the jointing stage. It is also necessary to select a feasible network model suitable for the precise identification and positioning of maize plants in the field. In this study, a novel device was developed for canopy recognition during the maize jointing stage using deep learning. A total of 3000 images of maize in the seedling and jointing stages were also taken to train the model of deep learning. Furthermore, a labeling strategy was proposed for the sample set, where the whole maize object was replaced with the center of the maize plant. Some data enhancement techniques were utilized to increase the robustness and generalization ability of the identification model for maize plants, including the passive brightness, chroma, rotation, mirroring, and blurring. After that, three datasets of maize plants were achieved, including 10 500 training sets, 3 000 cross-validation sets, and 300 test sets. In the model training, the SSDLite-MobileDet network model was first trained on the Google Colab cloud platform, and then compared with the SSDLite-MobileNetV3 model. Finally, an optimal SSDLite-MobileDet model was achieved with a speed of 110 frames/s and an accuracy of 92.4%. More importantly, some strategies were proposed to improve the performance of the model. 1) The specific procedure of extraction was visualized for the maize images during model training using a Convolutional Neural Network (CNN). It was found that the target information was significantly enriched in the output feature mapping, as the number of CNN layers deepened gradually. 2) The heart of the plant was labelled to avoid the serious leaf crossing at the jointing stage of maize. Thus, the novel model presented an average recognition accuracy of 92.4% under severe leaf crossing and different lighting conditions. 3) A rapid detection device was built for the maize crops with Raspberry Pi 4B+Coral USB, including Raspberry Pi 4B control, accelerator fast calculation, camera image acquisition, power, and image display module. As such, a rapid and accurate platform was obtained to collect, process, and display the images of the maize canopy in the field. 4) The model that trained on the GPU was quantized to transplant the trained model to the device. The data type of FP32 was converted to INT8, thereby ensuring that the quantized model occupied less memory. 5) A software was designed to run the SSDLite-MobileDet lightweight model on Raspberry Pi 4B, further to realize the acquisition, recognition, and display of maize images. The specific operation included system initialization, model loading, images acquisition, model processing, maize canopy recognition, positioning, and display. Finally, a field experiment was carried out to evaluate the rapid detection device for the maize crops, where the frame rate of the detected video was more than 89 frame/s, and the application recognition accuracy rate reached 91%. The findings can also offer strong support to the high-precision diagnosis and refined operation of maize in the field. [ABSTRACT FROM AUTHOR]