Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection

Abstract

Rapid detection and identification of the extent of spruce bark beetle infestation in the field is a necessity for researchers, forest rangers, and tree protection agencies. This paper presents an innovative solution that uses an autonomous multifunctional probe with embedded artificial intelligence. This approach is based on the real-time object detection method, called “first object, more object” (FOMO), which is adapted to the energy-efficient architecture of the ESP32 microcontroller. Neural network algorithms for fast image processing were trained using data sets from various locations in central Europe, which were heavily affected by the bark beetle calamity. The results of the experimental verification of the deployment of this smart probe in the field demonstrate a high level of precision in the detection and identification of the extent of damage.

Keywords

• Bark beetle
• Spruce
• Tree
• IoT
• Protection
• Neural networks