Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.
This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, [Formula: see text]) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors.
Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Jonas
http://orcid.org/0000-0002-5962-5326
J
Osterloff
Statoil ASA, Research and Technology, N-7005 Trondheim, Norway.|Trondhjem Biological Station, Department of Biology, Norwegian University of Science and Technology, N-7491 Trondheim, Norway.
Ingunn
I
Nilssen
Statoil ASA, Research and Technology, N-7005 Trondheim, Norway.
Ingvar
I
Eide
Instituto de Pesquisa Jardim Botanico do Rio de Janeiro, CEP 22.460-030, Rio de Janeiro, RJ, Brazil.|Instituto Biodiversidade Marinha, 22.793-000, Rio de Janeiro, RJ, Brazil.
Marcia Abreu
MA
de Oliveira Figueiredo
Instituto Biodiversidade Marinha, 22.793-000, Rio de Janeiro, RJ, Brazil.|Instituto de Estudos do Mar Almirante Paulo Moreira, 28.930-000, Arraial do Cabo, RJ, Brazil.
Frederico Tapajós
FT
de Souza Tâmega
Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Tim W
TW
Nattkemper
D004784:Environmental Monitoring / Q000295:instrumentation; D004867:Equipment Design; D019015:Geologic Sediments / Q000032:analysis; D000069550:Machine Learning; D010788:Photosynthesis; D045332:Photosystem II Protein Complex / Q000378:metabolism; D010865:Pilot Projects; D000461:Rhodophyta / Q000033:anatomy & histology / Q000502:physiology* / Q000528:radiation effects; D013312:Stress, Physiological; D013472:Sunlight
D045332:Photosystem II Protein Complex