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.

中文翻译：

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基于深度学习的水生植物识别技术与自然生态美学保护


水生植物在园林绿化生态系统的建设和维护中发挥着至关重要的作用，对园林生态系统的增强和保护做出了重大贡献。监测水生植物的生长和种群结构，可以评估水体的营养富集和水质污染情况，为水质改善和保护提供依据。然而，缺乏高效的自动化监测技术，限制了监测效率，无法满足大范围、长期监测的需求。深度学习技术为处理和分析水生植物监测数据提供了一种有前景的解决方案。通过训练深度学习模型，可以实现水生植物监测数据的自动化和分析，从而提高数据处理效率和准确性。本研究通过互联网获取水生植物图像数据，构建水生植物识别数据集。我们改进了DenseNet169模型来构建EFL-DenseNet模型。在ImageNet数据集上进行初步训练后，应用迁移学习使模型适应自建的水生植物识别数据集。最终模型的准确率达到了91.52%，与其他模型相比具有显着优势。