UAV remote sensing monitoring of pine forest diseases based on improved Mask R-CNN

ABSTRACT

Timely and accurate monitoring of pine forest diseases is greatly important to pine forest disease prevention and forest protection. A method for unmanned aerial vehicle (UAV) remote sensing monitoring of pine forest diseases is proposed based on improved Mask region-based convolutional neural network (R-CNN) to solve the problem of low accuracy of the existing methods due to complex background and nonevident characteristics of pine forest diseases. The Mask R-CNN with multitask capabilities is utilized as backbone network to build the monitoring model. An improved multiscale receptive field block module is added to the Mask R-CNN to extract the detailed features of pine forest diseases and reduce the missed detection of pine forest diseases. Meanwhile, a multilevel fusion residual pyramid structure is adopted to integrate low-level and high-level features to obtain distinguishable features of pine forest diseases to reduce the misdetection and misidentification of complex backgrounds. In addition, the proposed method uses cutting mixing splicing method to construct training data set to increase the number of background objects in the training images and reduce the impact of complex backgrounds on the monitoring results. Experimental results show that the proposed method can monitor pine forest diseases more accurately than methods of SSD, Faster R-CNN, MaskScoring R-CNN, and the original Mask R-CNN. Compared with the method of backbone network, the precision of the proposed method has increased by 22.4%, the recall has increased by 3.5%, and the F1-score has increased by 14.4%.

Keywords:

• Pine forest disease
• disease monitoring
• UAV remote sensing
• deep learning
• Mask R-CNN

Acknowledgements

The authors thank the Major Natural Science Research Projects in Colleges and Universities of Anhui Province under Grant KJ2020ZD03 and the Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application of Anhui University under Grant (AE201902) for their support.

Disclosure statement

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

Additional information

Funding

The work was supported by the Major Natural Science Research Projects in Colleges and Universities of Anhui Province, China [KJ2020ZD03]; Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application of Anhui University [AE201902].