{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:10:56Z","timestamp":1784301056961,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFD0600202"],"award-info":[{"award-number":["2018YFD0600202"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32071907"],"award-info":[{"award-number":["32071907"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BAAFS' Innovation Ability Construction Program 2018","award":["KJCX20200206"],"award-info":[{"award-number":["KJCX20200206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease\u2019s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder\u2013decoder architecture recovered location\/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.<\/jats:p>","DOI":"10.3390\/rs13183594","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T21:36:58Z","timestamp":1631223418000},"page":"3594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Lang","family":"Xia","sequence":"first","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8928-4238","authenticated-orcid":false,"given":"Ruirui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"National Center for International Research on Agricultural Aerial Application Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longlong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"National Center for International Research on Agricultural Aerial Application Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongchuan","family":"Yi","sequence":"additional","affiliation":[{"name":"Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"National Center for International Research on Agricultural Aerial Application Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wen","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenchen","family":"Ding","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"},{"name":"Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunchun","family":"Xie","sequence":"additional","affiliation":[{"name":"Shandong Ruida Pest Control Company Limited, Jinan 250000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"ref_1","first-page":"e00415","article-title":"Understanding pine wilt disease: Roles of the pine endophytic bacteria and of the bacteria carried by the disease-causing pinewood nematode","volume":"6","author":"Grass","year":"2016","journal-title":"MicrobiologyOpen"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"764","DOI":"10.3389\/fpls.2021.652500","article-title":"Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China","volume":"12","author":"Tang","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ding, X., Wang, Q., Guo, Y., Li, Y., Lin, S., Zeng, Q., Sun, F., Li, D.-W., and Ye, J. (2021). Copy Number Variations of Glycoside Hydrolase 45 Genes in Bursaphelenchus xylophilus and Their Impact on the Pathogenesis of Pine Wilt Disease. Forests, 12.","DOI":"10.3390\/f12030275"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1111\/ppa.12960","article-title":"Induction of resistance against pine wilt disease caused by Bursaphelenchus xylophilus using selected pine endophytic bacteria","volume":"68","author":"Kim","year":"2019","journal-title":"Plant Pathol."},{"key":"ref_5","first-page":"71","article-title":"Experiments on the relationship between the bacterium isolate B619 and the pine wilt disease by using Calli of Pinus thunbergii","volume":"5","author":"Guo","year":"2001","journal-title":"J. Nanjing For. Univ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hirata, A., Nakamura, K., Nakao, K., Kominami, Y., Tanaka, N., Ohashi, H., Takano, K., Takeuchi, W., and Matsui, T. (2017). Potential distribution of pine wilt disease under future climate change scenarios. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0182837"},{"key":"ref_7","unstructured":"(2021, May 21). National Forestry and Grassland Administration, Available online: http:\/\/www.forestry.gov.cn\/main\/5462\/20210521\/114505021470794.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.foreco.2016.12.027","article-title":"Monochamus galloprovincialis and Bursaphelenchus xylophilus life history in an area severely affected by pine wilt disease: Implications for forest management","volume":"389","author":"Firmino","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1890\/0012-9658(1999)080[1691:MTSOPW]2.0.CO;2","article-title":"Modeling the spread of pine wilt disease caused by nematodes with pine sawyers as vector","volume":"80","author":"Yoshimura","year":"1999","journal-title":"Ecology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118986","DOI":"10.1016\/j.foreco.2021.118986","article-title":"Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning","volume":"486","author":"Wu","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16645","DOI":"10.1007\/s11042-019-07976-5","article-title":"A hyperspectral GA-PLSR model for prediction of pine wilt disease","volume":"79","author":"Zhang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhang, Z., Zheng, L., Han, C., Wang, X., Xu, J., and Wang, X. (2020). Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques. Sensors, 20.","DOI":"10.3390\/s20133729"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"247","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-247-2020","article-title":"Extraction of the Individual Tree Infected by Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery","volume":"43","author":"Zhou","year":"2020","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, B., Ye, H., Lu, W., Huang, W., Wu, B., Hao, Z., and Sun, H. (2021). A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery. Remote. Sens., 13.","DOI":"10.3390\/rs13112083"},{"key":"ref_15","first-page":"61","article-title":"Recognition of wilt wood caused by pine wilt nematode based on U-Net network and unmanned aerial vehicle images","volume":"36","author":"Zhang","year":"2020","journal-title":"Trans. Chin. Soc. Agricult. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2005.03.007","article-title":"Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery","volume":"96","author":"White","year":"2005","journal-title":"Remote. Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1080\/01431160802566439","article-title":"Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery","volume":"30","author":"Hicke","year":"2009","journal-title":"Int. J. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.3390\/rs4051462","article-title":"Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing","volume":"4","author":"Kelcey","year":"2012","journal-title":"Remote. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Iordache, M.-D., Mantas, V., Baltazar, E., Pauly, K., and Lewyckyj, N. (2020). A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery. Remote. Sens., 12.","DOI":"10.3390\/rs12142280"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.eng.2020.07.001","article-title":"Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques","volume":"6","author":"Syifa","year":"2020","journal-title":"Engineering"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hu, G., Zhu, Y., Wan, M., Bao, W., Zhang, Y., Liang, D., and Yin, C. (2021). Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks. Geocarto Int., 1\u201320.","DOI":"10.1080\/10106049.2020.1864025"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, X., Tong, Z., Lan, Y., and Huang, Z. (2020). Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing. AgriEngineering, 2.","DOI":"10.3390\/agriengineering2020019"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8238","DOI":"10.1080\/01431161.2020.1766145","article-title":"Deep learning-based dead pine tree detection from unmanned aerial vehicle images","volume":"41","author":"Tao","year":"2020","journal-title":"Int. J. Remote. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119493","DOI":"10.1016\/j.foreco.2021.119493","article-title":"Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery","volume":"497","author":"Yu","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y., Lu, Q., and Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote. Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_31","unstructured":"Evan, S., and Trevor, D. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Philipp, F., and Thomas, B. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). Denseaspp for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene seg-mentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_39","unstructured":"Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., and Wang, J. (2018). Ocnet: Object context network for scene parsing. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compbiomed.2018.08.018","article-title":"Robust liver vessel extraction using 3D U-Net with variant dice loss function","volume":"101","author":"Huang","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:59:34Z","timestamp":1760165974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,9]]},"references-count":41,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183594"],"URL":"https:\/\/doi.org\/10.3390\/rs13183594","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,9]]}}}