{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:00:15Z","timestamp":1773806415724,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["No.21-Y30B02-9001-19\/22"],"award-info":[{"award-number":["No.21-Y30B02-9001-19\/22"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2019YFA0606600"],"award-info":[{"award-number":["No.2019YFA0606600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Emergency Science and Technology Projects of State Forestry and Grassland Administration","award":["ZD202001-06"],"award-info":[{"award-number":["ZD202001-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pine wood nematode disease is a devastating pine disease that poses a great threat to forest ecosystems. The use of remote sensing methods can achieve macroscopic and dynamic detection of this disease; however, the efficiency and accuracy of traditional remote sensing image recognition methods are not always sufficient for disease detection. Deep convolutional neural networks (D-CNNs), a technology that has emerged in recent years, have an excellent ability to learn massive, high-dimensional image features and have been widely studied and applied in classification, recognition, and detection tasks involving remote sensing images. This paper uses Gaofen-1 (GF-1) and Gaofen-2 (GF-2) remote sensing images of areas with pine wood nematode disease to construct a D-CNN sample dataset, and we train five popular models (AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and VGG16) through transfer learning. Finally, we use the \u201cmacroarchitecture combined with micromodules for joint tuning and improvement\u201d strategy to improve the model structure. The results show that the transfer learning effect of SqueezeNet on the sample dataset is better than that of other popular models and that a batch size of 64 and a learning rate of 1 \u00d7 10\u22124 are suitable for SqueezeNet\u2019s transfer learning on the sample dataset. The improvement of SqueezeNet\u2019s fire module structure by referring to the Slim module structure can effectively improve the recognition efficiency of the model, and the accuracy can reach 94.90%. The final improved model can help users accurately and efficiently conduct remote sensing monitoring of pine wood nematode disease.<\/jats:p>","DOI":"10.3390\/rs14040913","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:58:03Z","timestamp":1644872283000},"page":"913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Jixia","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"},{"name":"Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2647-9385","authenticated-orcid":false,"given":"Xiao","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5527-9123","authenticated-orcid":false,"given":"Liyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Hong","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Biological Disaster Prevention and Control, National Forestry and Grassland Administration, Shenyang 110034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8651-9505","authenticated-orcid":false,"given":"Shaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Guofei","family":"Fang","sequence":"additional","affiliation":[{"name":"Center for Biological Disaster Prevention and Control, National Forestry and Grassland Administration, Shenyang 110034, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"ref_1","unstructured":"Rodrigues, J.M. 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