Diagnosis of pine wilt disease using remote wireless sensing.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
05
2021
accepted:
13
09
2021
entrez:
24
9
2021
pubmed:
25
9
2021
medline:
20
11
2021
Statut:
epublish
Résumé
Pine wilt disease caused by Bursaphelenchus xylophilus is a major tree disease that threatens pine forests worldwide. To diagnose this disease, we developed battery-powered remote sensing devices capable of long-range (LoRa) communication and installed them in pine trees (Pinus densiflora) in Gyeongju and Ulsan, South Korea. Upon analyzing the collected tree sensing signals, which represented stem resistance, we found that the mean absolute deviation (MAD) of the sensing signals was useful for distinguishing between uninfected and infected trees. The MAD of infected trees was greater than that of uninfected trees from August of the year, and in the two-dimensional plane, consisting of the MAD value in July and that in October, the infected and uninfected trees were separated by the first-order boundary line generated using linear discriminant analysis. It was also observed that wood moisture content and precipitation affected MAD. This is the first study to diagnose pine wilt disease using remote sensors attached to trees.
Identifiants
pubmed: 34559856
doi: 10.1371/journal.pone.0257900
pii: PONE-D-21-15310
pmc: PMC8462718
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0257900Déclaration de conflit d'intérêts
One of the authors, Seong Bean Park, authored Korean Patent #10-2233454, “Method and system for forecasting tree disease based on water data”. Seong Bean Park is the Director of Research Laboratory at ECONNBIZ CO., LTD. We confirm that this does not alter our adherence to PLOS ONE policies on sharing data and materials.
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