<p>Pine wilt disease (PWD) devastates pine trees and poses a major threat to pine forests. Rapid diagnosis is crucial for effective disease management. To address this challenge, we developed and validated a novel plant biotechnology platform based on in situ wireless sensing, enabling near real-time tree health monitoring of wild pine trees. By deploying low-power resistance sensors on trees across multiple forest sites and analyzing long-term sensor data, we identified key diagnostic features—such as the mean resistance value, coefficient of variation, and daily trough time—that effectively differentiate healthy trees from those affected by PWD. This system enables continuous tracking of individual tree health over time. Simple machine learning model accurately predicted tree health status using only one week of short-term sensor data. Furthermore, healthy trees exhibited a strong negative correlation between air temperature and resistance sensor values, as well as lower sensor value variability than dead trees under similar air temperature fluctuations. These findings demonstrate the feasibility and power of biosensor-based biotechnology for early, scalable, and minimally invasive diagnosis of tree diseases.</p>