Browse > Article
http://dx.doi.org/10.12652/Ksce.2021.41.3.0317

Pine Wilt Disease Detection Based on Deep Learning Using an Unmanned Aerial Vehicle  

Lim, Eon Taek (The Korea Transport Institute)
Do, Myung Sik (Hanbat National University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.41, no.3, 2021 , pp. 317-325 More about this Journal
Abstract
Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.
Keywords
UAV; Pine wilt disease; YOLOv2; SegNet; Deep learning;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Son, M. H., Lee, W. K., Lee, S. H., Cho, H. K. and Lee, J. H. (2006). "Natural spread pattern of damaged area by pine wilt disease using geostatistical analysis." Journal of Korean Society of Forest Science, KFS, Vol. 95, No. 3, pp. 240-249 (in Korean).
2 Xu, C. X., Lim, J. H., Jin, X. M. and Yun, H. C. (2018). "Land cover mapping and availability evaluation based on drone images with multi-spectral camera." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, KSGPC, Vol. 36, No. 6, pp. 589-599 (in Korean).   DOI
3 Lee, S. K., Park, S. J., Baek, G. M., Kim, H. B. and Lee, C. W. (2019). "Detection of damaged pine tree by the pine wilt disease using UAV Image." Korean Journal of Remote Sensing, KSRS, Vol. 35, No. 3, pp. 359-373 (in Korean).   DOI
4 Badrinarayanan, V., Kendall, A. and Cipolla, R. (2017). "Segnet: A deep convolutional encoder-decoder architecture for image segmentation." IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, Vol. 39, No. 12, pp. 2481-2495.   DOI
5 Kim, S. R., Kim, E. S., Nam, Y. W., Choi, W. I. and Kim, C. M. (2015). "Distribution characteristics analysis of pine wilt disease using time series hyperspectral aerial imagery." Korean Journal of Remote Sensing, KSRS, Vol. 31, No. 5, pp. 385-394 (in Korean).   DOI
6 Kim, M. J., Bang, H. S. and Lee, J. W. (2017). "Use of unmanned aerial vehicle for forecasting pine wood nematode in boundary area: A case study of Sejong Metropolitan Autonomous City." Journal of Korean Society of Forest Science, KFS, Vol. 106, No. 1, pp. 100-109 (in Korean).   DOI
7 Kim, E. N. and Kim, D. Y. (2008). "An investigation of pine wilt damage by using ground remote sensing technique." Journal of the Korean Association of Regional Geographers, KRG, Vol. 14, No. 1, pp. 84-92 (in Korean).
8 Kim, J. B., Jo, M. H., Oh, J. S., Lee, K. J. and Park, S. J. (2001). "Extraction method of damaged area by pine wilt disease (Bursaphelenchus xylophilus) using remotely sensed data and GIS." In Proceedings of ACRS 2001, 22nd Asian Conference on Remote Sensing, Singapore.
9 Kim, J. B., Kim, D. Y. and Park, N. C. (2010). "Development of an aerial precision forecasting techniques for the pine wilt disease damaged area based on GIS and GPS." Journal of the Korean Association of Geographic Information Studies, KAGIS, Vol. 13, No. 1, pp. 28-34 (in Korean).   DOI
10 Kim, J. H., Seo, I. G. and Park, S. J. (2016). "An analysis on the situation of collection and utilization of the trees damaged by pine wilt disease." Journal of Environmental Science International, KENSS, Vol. 25, No. 1, pp. 127-134 (in Korean).   DOI
11 Kotsiantis, S., Kanellopoulos, D. and Pintelas, P. (2006). "Handling imbalanced datasets: A review." GESTS International Transactions on Computer Science and Engineering, Vol. 30, No. 1, pp. 25-36.
12 Lee, H. D. (2017). Identification of damaged trees by pine wilt diseases using drone images and GIS, Master's thesis, Graduate School of Kyungil University (in Korean).
13 Lee, J. B., Kim, E. S. and Lee, S. H. (2014). "An analysis of spectral pattern for detecting pine wilt disease using ground-based hyperspectral camera." Korean Journal of Remote Sensing, KSRS, Vol. 30, No. 5, pp. 665-675 (in Korean).   DOI
14 Nagai, M., Chen, T., Shibasaki, R., Kumagai, H. and Ahmed, A. (2009). "UAV-borne 3-D mapping system by multisensor integration." IEEE Transactions on Geoscience and Remote Sensing, IEEE, Vol. 47, No. 3, pp. 701-708.   DOI
15 Redmon, J. and Farhadi, A. (2017). "YOLO9000: better, faster, stronger." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 7263-7271.
16 Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M. and Dungey, H. S. (2017). "Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak." ISPRS Journal of Photogrammetry and Remote Sensing, ISPRS, Vol. 131, pp. 1-14.   DOI
17 Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). "You only look once: Unified, real-time object detection." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 779-788.
18 Rokhmana, C. A. (2015). "The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia." Procedia Environmental Sciences, ELSEVIER, Vol. 24, pp. 245-253.   DOI
19 Smigaj, M., Gaulton, R., Suarez, J. C. and Barr, S. L. (2019). "Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity." Forest Ecology and Management, ELSEVIER, Vol. 433, pp. 699-708.   DOI
20 Derczynski, L. (2016). "Complementarity, F-score, and NLP evaluation." Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), European Language Resources Association (ELRA), pp. 261-266.
21 Do, M. S., Lim, E. T., Chae, J. H. and Kim, S. H. (2018). "Accuracy evaluation and terrain model creation of urban space using unmanned aerial vehicle system." The Journal of the Korea Institute of Intelligent Transport Systems, KITS, Vol. 17, No. 5, pp. 117-127 (in Korean).   DOI
22 Dyson, J., Manc ini, A., Frontoni, E. and Zingaretti, P. (2019). "Deep learning for soil and crop segmentation from remotely sensed data." Remote Sensing, MDPI, Vol. 11, No. 16, pp. 1859.   DOI
23 Giuffrida, G., Meoni, G. and Fanucci, L. (2019). "A YOLOv2 convolutional neural network-based human-machine interface for the control of assistive robotic manipulators." Applied Sciences, MDPI, Vol. 9, No. 11, pp. 2243.   DOI
24 Kim, D. I., Song, Y. S., Kim, G. H. and Kim, C. W. (2014). "A study on the application of UAV for Korean land monitoring." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, KSGPC, Vol. 32, No. 1, pp. 29-38 (in Korean).   DOI
25 Han, S. H. (2019). "Project design plan for drone photogrammetry." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 39, No.1, pp.239-246 (in Korean).   DOI
26 Im, S. H., Hassan, S. I., Minh, D. L., Min, K. B. and Moon, H. J. (2018). "Analysis of fusarium wilt based on normalized difference vegetation index for radish field images from unmanned aerial vehicle." The Transactions of The Korean Institute of Electrical Engineers, KIEE, Vol. 67, No. 10, pp. 1353-1357.   DOI