An improved YOLO model for detecting trees suffering from pine wilt disease at different stages of infection

ABSTRACT

Pine wilt disease (PWD) is one of the most destructive forest diseases in the world. Therefore, timely monitoring of PWD is essential to the preservation of the ecological environment. However, the complex topography of PWD-outbreak locations affords many limitations in the manual detection of the disease. The use of unmanned aerial vehicles (UAVs) and deep learning technology to detect PWD-infected trees has gained popularity in recent years. In this study, we configured the You Only Look Once version 3 (YOLOv3) model according to the characteristics of the three disease stages of PWD and proposed an improved model called Effi_YOLO_v3. The results revealed that the improved model achieved good detection performance. The proposed model yielded a mean average precision (mAP) of 94.39%, and the classification of the different infection stages was relatively accurate. The recall values for the classifications of trees in the early-infection, late-infection, and death stages were 89.15%, 86.13%, and 86.77%, respectively. This indicates that the model offers good applicability in detecting different stages of PWD in trees.

Acknowledgments

The authors would like to acknowledge the funding from the National Natural Science Foundation of China. The authors would like to acknowledge the funding from the Characteristic Innovation Projects of Universities in Guangdong Province. The authors would also like to thank the developers in the ArcGIS and Pytorch developer communities for their open source projects.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated and analysed during the current study are not publicly available due to the need for further research, but are available from the corresponding author on reasonable request.

Geolocation information

483 Wushan Road, Tianhe District, Guangzhou, 510642, China.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 32271985] and in part by the Special Project in key Areas of Artificial Intelligence in Guangdong Universities [grant number 2019KZDZX1002].