Diagnosis of pine wilt disease using remote wireless sensing.

Sang-Kyu Jung,Seong Bean Park,Bong Sup Shim,Chi-Tsun Cheng

doi:10.1371/journal.pone.0257900

Sang-Kyu Jung, Seong Bean Park + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0257900

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Journal: PloS one	Publication Date: Sep 24, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Hongik University, Korea Health Promotion Institute

Abstract

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.

Highlights

Pine wilt disease (PWD) is one of the major plant diseases that, despite years of research and control efforts, constantly threaten pine forests in Japan, China, Canada, and Europe [1,2,3,4]
PWD is caused by the pine wood nematode, Bursaphelenchus xylophilus, which is transferred to trees by vector insects such as Monochamus alternatus and Monochamus saltuarius [1,5]
Upon analyzing the collected sensing data, we found that there was a difference in the changes in the sensing signals of uninfected and infected pine trees, and that the mean absolute deviation (MAD) could be used to distinguish between the two classes

Summary

Introduction

Pine wilt disease (PWD) is one of the major plant diseases that, despite years of research and control efforts, constantly threaten pine forests in Japan, China, Canada, and Europe [1,2,3,4]. We (1) developed a battery-powered remote sensing device, (2) attached the device to wild pine trees in a forest, and measured sensing data of the trees at regular intervals, (3) collected sensing data from a distance through long-range (LoRa) communication in real time, and (4) developed a technology to diagnose infected trees by performing statistical analysis of processed sensing signals. Upon analyzing the collected sensing data, we found that there was a difference in the changes in the sensing signals of uninfected and infected pine trees, and that the mean absolute deviation (MAD) could be used to distinguish between the two classes.

Results

Conclusion