Intelligent identification and early warning of Ptyomaxia syntaractis based on YOLO-V5
Ptyomaxia syntaractis,the main pest of the mangrove plants Avicennia marina,affected the growth and ecological function of A.marina seriously.In order to efficiently monitor the population dynamics,obtain the early warning information and publish population numbers in real time,object detection algorithm YOLO V5 was introduced for deep learning to identify and count the moth on the monitoring equipment in this study.Black light trapping devices were used to obtain the adult images of P.syntaractis,and two datasets with different image sizes,enhanced by means of rotation and noise enhancement were constructed.The detection performance of different training models on acquired images and the effect of different image sizes on the recognition results of datasets were compared,and accuracy,recall rate,F1 value and average accuracy were used to evaluate the differences among the models.The results showed that the accuracy,recall rate and F1 value of YOLO V5s model for the identification P.syntaractis were 96.13％,92.06％and 0.93 respectively,and the model could well recognize the original size image.The identification and counting model based on YOLO V5 algorithm can be used in the population monitoring for its high recognition accuracy.
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