Development and Application of an Automated System for Early Detection of Stress and Damage in Poplar Clone Plantations Using Eco-Physiological Sensors and IoT

This paper presents a study focused on the development and application of a system for monitoring stress and damage in poplar clone plantations using eco-physiological sensors and the Internet of Things (IoT). The research aimed to develop a code for automated data processing and anomaly detection, calibrate the system in poplar plantations, and explore the potential use of sensor data variables as part of an early warning system. The study area included three poplar plantations in the province of León, Spain, and the TreeTalker+ (TT+) devices were utilized to measure various parameters of tree health and environmental conditions. The developed R programming code enabled efficient data processing and identification of abnormal patterns, while calibration ensured accurate measurements. The collected sensor data showed promise for detecting early signs of stress or damage in poplar trees. The findings emphasize the significance of integrating IoT technologies and eco-physiological sensors for real-time monitoring and informed decision-making in forest management. Further research and validation are recommended to enhance system accuracy and broaden its applicability.