{"corpus_id":259744692,"paper_sha":"02de01a4aec0e8a25c93300df07d3aeacef97ab1","doi":"10.3390/rs15133295","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":null,"dblp_id":"journals/remotesensing/LiCYLLZ23","acl_id":null,"title":"Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery","year":2023,"publication_date":"2023-06-27","venue":"Remote Sensing","journal":{"name":"Remote. Sens.","pages":"3295","volume":"15"},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science","Engineering","Environmental Science"],"reference_count":37,"citation_count":31,"influential_citation_count":3,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"https://www.mdpi.com/2072-4292/15/13/3295/pdf?version=1687868195","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/02de01a4aec0e8a25c93300df07d3aeacef97ab1","s2_open_access_license":"CCBY","s2_open_access_status":"GOLD","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD.","claims":[{"public_id":"cl_01a2ddc28b327a21ac999ea376a9c315","status":"active","text":"An improved Mask R-CNN instance segmentation method and an integrated method combining prototypical network classification with individual tree segmentation were evaluated for identifying PWD-infected individual trees at different disease stages using UAV-based hyperspectral imagery.","confidence":0.95,"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_01a2ddc28b327a21ac999ea376a9c315"},{"public_id":"cl_ad5f932b28437f46d4fbb3c7ac0ca53f","status":"active","text":"Early infected pine trees were identified with 74.89% accuracy by the integrated prototypical network approach.","confidence":0.94,"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_ad5f932b28437f46d4fbb3c7ac0ca53f"},{"public_id":"cl_c4107412a0dd6dc8cede08025eed644e","status":"active","text":"The improved Mask R-CNN achieved 71% overall accuracy for pine wilt disease identification when screened bands data were used as input.","confidence":0.96,"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_c4107412a0dd6dc8cede08025eed644e"},{"public_id":"cl_ae37bfad4cb39325524e7ebfa634d81c","status":"active","text":"The integrated method achieved 83.51% overall accuracy for pine wilt disease identification based on screened bands data.","confidence":0.96,"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_ae37bfad4cb39325524e7ebfa634d81c"}],"concepts":[{"public_id":"co_1a7bec8c81a5c096636af1f8dc646716","status":"active","name":"screened bands data","description":"Selected hyperspectral bands used as input data for pine wilt disease identification models.","types":["input data"],"aliases":["screened bands"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_1a7bec8c81a5c096636af1f8dc646716"},{"public_id":"co_1cfec3152ea83540b9565a879e1314da","status":"active","name":"early infected pine trees","description":"Pine trees at an early stage of pine wilt disease infection targeted for identification.","types":["disease stage"],"aliases":[],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_1cfec3152ea83540b9565a879e1314da"},{"public_id":"co_46e09d211633707cbace3caf9258b488","status":"active","name":"hyperspectral sensors","description":"Sensors carried by UAVs that collect rich spatial and spectral information about pine trees.","types":["remote sensing instrument"],"aliases":[],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_46e09d211633707cbace3caf9258b488"},{"public_id":"co_99a78b9011dd545162956242ec3afddf","status":"active","name":"pine wood nematode","description":"An invasive nematode species associated with pine wilt disease in China.","types":["organism"],"aliases":["PWN","Bursaphelenchus xylophilus"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_99a78b9011dd545162956242ec3afddf"},{"public_id":"co_a0067754f3a1acbdfba881d4e7fdd885","status":"active","name":"improved Mask R-CNN instance segmentation method","description":"A modified Mask R-CNN approach used for instance segmentation of pine wilt disease infected individual trees.","types":["segmentation method"],"aliases":["improved Mask R-CNN"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_a0067754f3a1acbdfba881d4e7fdd885"},{"public_id":"co_ae2365f7d86d8a789b5189ca958d29ef","status":"active","name":"prototypical network classification model","description":"A classification model used as part of an integrated approach for identifying infected individual pine trees.","types":["classification model"],"aliases":[],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_ae2365f7d86d8a789b5189ca958d29ef"},{"public_id":"co_b7218614c23839540bc45135bcad1b74","status":"active","name":"pine wilt disease","description":"A disease of pine trees monitored in this work through remote sensing and deep learning methods.","types":["plant disease"],"aliases":["PWD"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_b7218614c23839540bc45135bcad1b74"},{"public_id":"co_c974f9a1639bd411844a3b4625630bda","status":"active","name":"integrated method combining prototypical network classification model with individual tree segmentation algorithm","description":"A combined approach that pairs a prototypical network classifier with individual tree segmentation for pine wilt disease identification.","types":["integrated method"],"aliases":["integrated prototypical network method","integrated approach"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_c974f9a1639bd411844a3b4625630bda"},{"public_id":"co_cdd32175f7a40d9e6832ddcc1d1566f2","status":"active","name":"UAV-based hyperspectral imagery","description":"Near-ground remote sensing imagery collected by unmanned aerial vehicles carrying hyperspectral sensors.","types":["remote sensing data"],"aliases":["UAV hyperspectral data"],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_cdd32175f7a40d9e6832ddcc1d1566f2"},{"public_id":"co_f4329617ae881e968500abc951ee3e29","status":"active","name":"individual tree segmentation algorithm","description":"An algorithm used to delineate individual trees before classification in the integrated identification approach.","types":["segmentation algorithm"],"aliases":[],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_f4329617ae881e968500abc951ee3e29"},{"public_id":"co_fbd38af79c06f0ac30efaffdbe76b7ba","status":"active","name":"deep learning techniques","description":"Machine learning techniques used to mine multidimensional data features for classification and target identification.","types":["computational method"],"aliases":[],"contributors":[{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["extraction"],"url":"https://sah.borca.ai/u/b2adb6bfad"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_fbd38af79c06f0ac30efaffdbe76b7ba"}],"external_ids":{"DOI":"10.3390/rs15133295","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":null,"DBLP":"journals/remotesensing/LiCYLLZ23","ACL":null},"open_access":{"is_open_access":true,"pdf_url":"https://www.mdpi.com/2072-4292/15/13/3295/pdf?version=1687868195","landing_url":"https://www.semanticscholar.org/paper/02de01a4aec0e8a25c93300df07d3aeacef97ab1","source":"semantic_scholar","pdf_url_source":"semantic_scholar_open_access_pdf","license":"CCBY","status":"GOLD","reason":null},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":631946,"paper_uid":"c8ba0ef6-ab89-4895-8e2a-224531964711","canonical_identity":{"paper_id":631946,"paper_uid":"c8ba0ef6-ab89-4895-8e2a-224531964711","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/259744692"}