Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 35001, Spain; Hydrology and Environmental Hydraulics Group, Wageningen University, Wageningen, 6708, BP, the Netherlands.
Hydrology and Environmental Hydraulics Group, Wageningen University, Wageningen, 6708, BP, the Netherlands.
Mar Pollut Bull. 2024 Oct;207:116914. doi: 10.1016/j.marpolbul.2024.116914. Epub 2024 Sep 7.
Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and characterizing macroplastics in aquatic environments. Despite numerous studies focusing on bands of interest in the shortwave infrared spectrum, the high cost of sensors in this range makes it difficult to mass-produce them for long-term and large-scale applications. Therefore, we present the assessment and transfer of various machine learning models across four datasets to identify the key bands for detecting and classifying the most prevalent plastics in the marine environment within the visible and near-infrared (VNIR) range. Our study uses four different databases ranging from virgin plastics under laboratory conditions to weather plastics under field conditions. We used Sequential Feature Selection (SFS) and Random Forest (RF) models for the optimal band selection. The significance of homogeneous backgrounds for accurate detection is highlighted by a 97 % accuracy, and successful band transfers between datasets (87 %-91 %) suggest the feasibility of a sensor applicable across various scenarios. However, the model transfer requires further training for each specific dataset to achieve optimal accuracy. The results underscore the potential for broader application with continued refinement and expanded training datasets. Our findings provide valuable information for developing compelling and affordable detection sensors to address plastic pollution in coastal areas. This work paves the way towards enhancing the accuracy of marine litter detection and reduction globally, contributing to a sustainable future for our oceans.
海洋塑料污染带来了重大的生态、经济和社会挑战，需要创新的检测、管理和缓解解决方案。光谱成象和光学遥感已被证明是在水生环境中检测和描述大塑料的有用工具。尽管有许多研究集中在短波红外光谱的感兴趣波段，但该范围内传感器的高成本使得难以大规模生产用于长期和大规模应用的传感器。因此，我们评估和转移了四个数据集上的各种机器学习模型，以确定用于在可见和近红外（VNIR）范围内检测和分类海洋环境中最常见塑料的关键波段。我们的研究使用了四个不同的数据库，范围从实验室条件下的原始塑料到野外条件下的风化塑料。我们使用顺序特征选择（SFS）和随机森林（RF）模型进行最佳波段选择。均匀背景对准确检测的重要性通过 97%的准确率得到了强调，并且数据集之间成功的波段转移（87%-91%）表明了适用于各种场景的传感器的可行性。然而，模型转移需要针对每个特定数据集进行进一步的训练，以达到最佳的准确性。研究结果突出了随着进一步的改进和扩展的训练数据集的应用潜力。我们的研究结果为开发有吸引力和负担得起的检测传感器提供了有价值的信息，以解决沿海地区的塑料污染问题。这项工作为提高海洋垃圾检测的准确性和减少全球塑料污染铺平了道路，为我们的海洋的可持续未来做出了贡献。
