Abstract:
Objective In order to achieve rapid and accurate identification of pests and diseases in the early disease stage of bergamot in complex background, we proposes a YOLOv5-C-based method for the identification of bergamot pests and diseases.
Method The YOLOv5s network model was used as the base network. The multi-scale feature fusion module was proposed and introduced to improve the feature extraction and feature fusion capability of the network model, and to improve the recognition accuracies of different bergamot pests and diseases in a balanced manner. The attention mechanism module was used to upgrade the attention degree of the network model to the information of target features of pests and diseases, weaken the interference information of complex background, and raise the recognition accuracy of the network model. An improved C3-SC module was used to replace the C3 module in the PANet structure to decrease the number of parameters in the network model without lowering the network model recognition performance.
Result F1 score of 90.95% and mean average precision of 93.06% were achieved when identifying the bergamot pests and diseases under a complex background using the YOLOv5-C method. The size of network model was 14.1 Mb, and the average detection time was 0.01 s per image on the GPU. Comparing with the original YOLOv5s, the mean accuracy of YOLOv5-C increased by 2.45 percentage point, the standard deviation of the average precision for seven categories was reduced from 7.14 to 3.13, and the coefficient of variation decreased from 7.88% to 3.36%. Moreover, the mean average accuracy was 22.30, 20.65, 4.84 and 2.36 percentage point higher than those of RetinaNet, SSD, Efficientdet and YOLOv4, respectively.
Conclusion The method can quickly and accurately identify bergamot pests and diseases under complex background, and provide a reference for intelligent management of bergamot cultivation industry.
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