Low Latency and Non-Intrusive Accurate Object Detection in Forests

A resilient and healthy forest requires accurate and timely monitoring, observing key forest health indicators (FRI). Forest managers and rangers usually perform tedious manual data collection using citizen science for biodiversity conservation and ecological research. With the advent of faster radio network technologies such as 4G, it is advantageous to leverage these networks' high speed and low latency for real-time monitoring. We present a novel approach to stream high definition videos over cellular networks to provide real-time (< 0.5 seconds) data transmission to the YOLOv5 machine learning algorithm, hosted in the cloud. The system provides non-intrusive precise tree class detection, matching existing models such as Fast R-CNN and SSD. Our investigation also reveals that the same accuracy can be achieved with 99 % fewer iterations, minimizing computational time and cost.