Monitoring Seagrass Health Using Neural Networks

H. Ressom, S. K. Fyfe, P. Natarajan, S. Srirangam

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


Monitoring seagrass health gives vital clues about the estuarine water quality, which is crucial for the existence of many aquatic plants and animals. Photosynthetic efficiency is a measure of plant stress and can be used to monitor seagrass health. However, insitu measurements of photosynthetic efficiency are time consuming and expensive. In this paper, neural network-based models are developed to estimate photosynthetic efficiency of a seagrass species, Zostera capricorni, from spectral reflectance measurements. The proposed neural network-based approach can be extended for other seagrass species by combining an ensemble of neural networks with a classifier. After identifying the type of seagrass species using the classifier, the neural network model that corresponds to the identified species is used to estimate photosynthetic efficiency.

Original languageEnglish (US)
Number of pages6
StatePublished - 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003


OtherInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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