Deep learning-based aquatic plant recognition technique and natural ecological aesthetics conservation

Abstract

Aquatic plants play a crucial role in the construction and maintenance of ecological systems in landscaping, contributing significantly to the enhancement and preservation of garden ecosystems. Monitoring the growth and population structure of aquatic plants enables the assessment of nutrient enrichment and water quality pollution in water bodies, serving as a basis for water quality improvement and protection. However, the lack of efficient automated monitoring technologies limits the efficiency of monitoring and fails to meet the demands of extensive and long-term monitoring. Deep learning techniques offer a promising solution for processing and analyzing aquatic plant monitoring data. By training deep learning models, automation and analysis of aquatic plant monitoring data can be achieved, thereby enhancing data processing efficiency and accuracy. In this study, aquatic plant image data were obtained through the internet to construct a dataset for aquatic plant recognition. We improved the DenseNet169 model to construct the EFL-DenseNet model. After initial training on the ImageNet dataset, transfer learning was applied to adapt the model to the self-built aquatic plant recognition dataset. The final model achieved an accuracy of 91.52%, demonstrating significant advantages over other models.