Towards Smart Monitoring of Invasive Aquatic Plants: A Comparative Evaluation of YOLOv11 and Faster R-CNN for Water Hyacinth Detection

In recent years, the proliferation of water hyacinths across various freshwater bodies has become an escalating environmental concern due to its adverse effects on agricultural productivity, aquatic ecosystems, and public health. As a highly invasive aquatic weed, water hyacinth rapidly spreads, obstructing irrigation canals, drainage systems, and fish farms, depleting oxygen levels, and degrading water quality, thereby directly challenging the objectives of Sustainable Development Goal (SDG) 6, which calls for improved water quality and the protection of water-related ecosystems. Early detection is essential for controlling its spread, yet accurately distinguishing water hyacinths from other aquatic vegetation remains technically difficult. To address this, this study evaluates the effectiveness of deep learning models in the rapid and accurate detection of water hyacinths. Specifically, we conduct a comparative analysis of YOLOv11n and Faster R-CNN using two datasets: a custom WaterHyacinth dataset and the Kaggle Water Hyacinth dataset. The models are assessed based on accuracy, inference speed, model size, and mean average precision (mAP). Our findings indicate that YOLOv11n outperforms Faster R-CNN, achieving 96.3% and 93.8% accuracy, on the respective datasets. Furthermore, YOLOv11n’s lightweight architecture and faster inference speed make it more suitable for real-time applications, highlighting its potential as a practical tool for the early detection and management of water hyacinth, thus contributing to SDG 6’s mission of ensuring clean water and healthy freshwater ecosystems.