In order to improve the recognition rate of plant leaf disease detection by edge computing devices, this study uses a convolutional neural network to build a plant leaf target recognition model and a plant leaf disease classification model and adopts OpenCV to integrate the two models into a plant leaf disease detection system. The target areas of plant leaves are positioned and clipped by the single shot multibox detector (SSD) algorithm, and then the plant leaf disease classification model is used to classify the clipped plant leaf areas according to diseases. At the same time, the classification model is optimized by TensorRT accelerated inference. In addition, on the same host device and Jetson Nano computing platform, a comparative experiment is carried out on the model before and after optimization. The experiment shows that the recognition rate of the optimized plant classification model on the same host device increases by 22 times. At the same time, the optimized classification model improves the recognition rate of the plant leaf disease detection system by seven times. Furthermore, the optimized system is deployed on the Jetson Nano computing platform, and the detection rate of plant leaf diseases is increased by 10 times compared with that before optimization, which thus realizes real-time plant leaf disease detection.