Lightweight detection of Panax notoginseng disease based on improved SSD algorithm

Objective  Aiming to address the problem of the current Panax notoginseng disease identification model, with complex structure and large number of parameters, hindering deployment on mobile devices, an improved model based on single shot multibox detector (SSD) target detection is proposed to enable convenient, fast and accurate P. notoginseng disease detection.

Method  Based on the SSD model architecture, the original feature extraction network (VGG16) was replaced by a lightweight convolutional neural network (MobileNet) to reduce the number of parameters and computation amount of the backbone network. Meanwhile, the RFB module was constructed based on the functional relationship between the size of the population wise receptive field (pRF) in human visual cortex and its eccentricity in the retinogram. The top convolutional layer of the original SSD model framework was replaced by the RFB module to enhance the deep features of the network, improve the detection accuracy and detection speed of the lightweight model, and enable multi-scale P. notoginseng disease detection.

Result  Compared with the SSD model, the RFB-MobileNet-SSD model reduced the number of network parameters and the computation amount by 96.67% and 96.10% respectively. The model validation using four different disease data under different weather conditions revealed that the improved model improved the accuracy by 4.6 percentage point, recall by 6.1 percentage point, F1 accuracy by 5.4 percentage point, and the time of single image detection was shortened from 0.073 s of the SSD model to 0.020 s, and the size was only 54.6% of the SSD model.

Conclusion  The improved model not only meets the purpose of real-time detection ofP. notoginseng leaf diseases, but is also more convenient for deployment in mobile devices. Moreover, RFB-MobileNet-SSD shows improved performance for small area disease detection and is more resistant to interference in complex environments, making it more suitable for P. notoginseng disease detection in field environment.