An Autonomous Marine Mucilage Monitoring System

Sanver U., Yeşildirek A.

Sustainability (Switzerland), cilt.15, sa.4, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 4
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/su15043340
  • Dergi Adı: Sustainability (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: marine mucilage monitoring, image processing, environmental remote sensing, sensor fusion
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Mucilage bloom is a current issue, especially for countries in the Mediterranean Basin, due to economic activities and ecological effects. The main causes are increased nutrient load due to organic and industrial pollution in the sea, global warming, and meteorological conditions at a level that can trigger mucilage bloom. It is important to take permanent measures to combat the increased nutrient load causing mucilage. However, there are various actions that can be performed during the mucilage bloom period, especially the collection of mucilage on the sea surface. Surface vehicles can be used to monitor and collect mucilage on the sea surface. The aim of this study is to design an autonomous marine mucilage monitoring system for systems such as unmanned surface vehicles (USV). We suggest monitoring the risky Marmara Sea continuously and recording some of the key parameters using a USV. The onboard solution proposed in this study has an architect based on a three-tier mucilage monitoring system. In the first tier, the sea surface is scanned with camera(s) in a certain radius in real time. When mucilage-candidate areas are determined, the vehicle is directed to this region autonomously. In the second tier, seawater in the region is measured in real time with some onboard sensors, pH level, conductivity, and dissolved oxygen level. The third tier is where real samples at three different depths are collected (if possible) for detailed posterior lab analysis. We have compared image processing, CNN (ResNet50), kNN, SVM, and FFNN approaches and have shown that the accuracy of our proposed mucilage classification method offers better and more promising performance.