Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks

Authors

  • Lucas Wayne Welch University of North Florida
  • Xudong Liu University of North Florida
  • Indika Kahanda University of North Florida
  • Sandeep Reddivari University of North Florida
  • Karthikeyan Umapathy University of North Florida

DOI:

https://doi.org/10.32473/flairs.v34i1.128498

Keywords:

Machine Learning, Neural Network, Convolutional Neural Network, Image Recognition, Environmental Monitoring

Abstract

Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process.
In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41\% for a AlexNet variant ondistinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.

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Published

2021-04-18

How to Cite

Welch, L. W., Liu, X., Kahanda, I., Reddivari, S., & Umapathy, K. (2021). Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128498

Issue

Section

Main Track Proceedings