TY  - JOUR
AB  - 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, ) 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.
DA  - 2016
DO  - 10.1371/journal.pone.0157329
LA  - eng
IS  - 6
PY  - 2016
SN  - 1932-6203
T2  - PLOS ONE
TI  - Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29037122
Y2  - 2024-11-22T10:57:24
ER  -