Effective pest plant management in conservation has long been hindered by challenges in monitoring and reporting, often resulting in inconsistent, low-resolution, or missing data. This lack of reliable information compromises decision-making and threatens the long-term sustainability of weed control efforts.

Two key barriers contribute to these challenges. First, traditional data collection is resource-intensive and costly, leading many projects to prioritize on-ground control over monitoring. Second, variability in practitioner skills and technological adoption makes standardising data collection difficult.

This presentation explores three new case studies (one from the Americas and two from Australia) demonstrating how organizations have successfully tackled these issues using Australian-made data logging technology. By seamlessly integrating with conservation tools, this system automatically records usage data, which is then transmitted to a web-based spatial platform for easy access and analysis. We assess the efficiency of data collection, its impact on project management, and the analytical potential of temporal and spatial data.

Requiring minimal operator training and no additional field time, this technology streamlines project tracking, enables real-time decision-making, and provides a definitive, universally accessible record of work. Beyond monitoring, the collected data offers valuable insights into weed cover, distribution, and diversity, while also serving as a robust dataset for research in weed ecology and remote sensing applications.

By adopting automated data collection, pest plant management practitioners can enhance monitoring, improve reporting accuracy, and optimise conservation outcomes.