Diagnosis of pine wilt disease using remote wireless sensing

Area of Science:

• Forest Pathology
• Remote Sensing Technology
• Plant Disease Diagnostics

Background:

• Pine wilt disease, caused by Bursaphelenchus xylophilus, poses a significant global threat to pine forests.
• Early and accurate diagnosis is crucial for managing and mitigating the spread of this devastating tree disease.

Purpose of the Study:

• To develop and validate a novel remote sensing method for diagnosing pine wilt disease in Pinus densiflora.
• To assess the efficacy of tree-attached sensors in distinguishing between healthy and infected pine trees.

Main Methods:

• Battery-powered remote sensing devices with long-range (LoRa) communication were installed on pine trees in South Korea.
• Stem resistance signals were collected and analyzed, focusing on the mean absolute deviation (MAD).
• Linear discriminant analysis was employed to differentiate between infected and uninfected trees based on MAD values over time.

Main Results:

• The mean absolute deviation (MAD) of sensor signals proved effective in distinguishing between uninfected and infected pine trees.
• Infected trees exhibited a higher MAD compared to uninfected trees starting from August.
• A clear separation boundary was established using July and October MAD values, with environmental factors like wood moisture and precipitation influencing MAD.

Conclusions:

• This study presents the first successful diagnosis of pine wilt disease using remote sensors directly attached to trees.
• The developed remote sensing approach offers a promising tool for early detection and monitoring of pine wilt disease in forest ecosystems.