May 31, 2024

Can Artificial Intelligence Help Save Eelgrass from Wasting Disease? 

This is a guest blog by Templeton High School senior Seraphina Jarboe, who interned at Cuesta College to further her interest in molecular science and computers.

It seems like every day we see stories in the news about artificial intelligence (AI). While some applications of this new tool have raised concerns, AI also has the potential to streamline scientific research. Eelgrass Lesion Image Segmentation Application (EeLISA) is a machine learning algorithm developed by Cornell University, and this AI tool may be key to efficiently detecting indications of wasting disease on eelgrass blades in Morro Bay. 

The Importance of Eelgrass

Eelgrass provides food and shelter for a wide variety of marine life.

Eelgrass is crucial to the Morro Bay ecosystem. This flowering plant serves as a food source and shelter for a variety of organisms, prevents erosion, filters pollutants and excess nutrients, and serves as a powerful sink for greenhouse gases. You can learn more about this cornerstone species and how it’s doing in Morro Bay at this link. 

In the past decade, eelgrass in Morro Bay underwent a drastic decrease and a rapid recovery. The decline may likely be attributed at least in part to Labyrinthula zosterae, an opportunistic fungus that causes wasting disease in eelgrass. The impacts of wasting disease are not a new phenomenon, being first described on the Atlantic coast of North America in the 1930s. I joined a Cuesta College research project working to better understand the factors that might trigger wasting disease in Morro Bay.  


Assessing disease in eelgrass samples can be time-consuming. Cuesta College researchers and students survey the estuary for wasting disease every summer, generating 350 images each year that would take several weeks to analyze manually.  

To speed up this process and receive data in real time, we reached out to Brendan Rapazzo, a researcher in Dr. Carla Gomes’ group at Cornell University. They had developed an AI called EeLISA (Eelgrass Lesion Image Segmentation Application) to automate this process. 

EeLISA takes an image of eelgrass blade fragments and assesses the color to detect the plant health.  

The eelgrass plant pictured on the bottom is bright green and free from dark brown and black areas on its blades. In comparison, the plant on the top has dark areas that indicate the presence of necrotic tissue that has died. Wasting disease can play a role in this process.

While blade colors within a certain range of greens are considered healthy, colors such as red, orange, yellow, and black can indicate tissue death (necrosis). 

The AI creates a simplified version of the plant color (shown on the bottom) into a “mask” (shown on the top). Healthy tissue is represented as green and dead tissue is shown in red. These pixels are then counted to provide an accurate area of the eelgrass blade that is necrotic and potentially affected by the wasting disease.

Customizing the AI for Morro Bay

EeLISA was created and optimized for eelgrass collected in other coastal areas in the US, so we weren’t surprised that it was less accurate for Morro Bay eelgrass. This could be due to how the images were taken and regional differences in eelgrass and wasting disease appearance.  

Initially the AI was able to accurately analyze only 1,057 out of 1,395 eelgrass fragments when compared to a trained human eye, which is only 76% accuracy. We then added additional parameters to optimize the AI for our samples, including changing the color threshold to reduce the necrosis false positives and expanding the range of healthy colors to include lighter greens. After this customization, EeLISA’s accuracy rate rose to 97%.  

Moving Forward…

We’ve taken thousands of images of eelgrass samples from past years, and earlier methods of analysis included manual processing which was time consuming and error-prone. 

In contrast, EeLISA can accurately analyze thousands of images approximately 5,000 times faster than manual methods, reducing months of painstaking labor to mere minutes. EeLISA also has the potential to standardize this process between groups, making data more easily comparable between locations or researchers.  

Aiden uses a digital microscope to show me a sample of eelgrass from the Morro Bay estuary infected with wasting disease. 

Author Bio: 

Seraphina Jarboe, a graduating senior at Templeton High School, conducted a year-long research project at Cuesta College with Dr. Silvio Favoreto and laboratory assistant Aiden Arroyo. She especially enjoyed learning how scientific research works and how biology intersects with fields like computer science. She plans on going to university this fall to study bioengineering. Seraphina hopes to continue studying eelgrass and learn how to better protect this key species. In her free time, she loves swimming, spending time with friends and family, and drawing.  

The Cuesta College team would like to acknowledge our funding support from the National Science Foundation, Award Number 2236402. We would also like to acknowledge the Cuesta College Foundation and the Estuary Program for their support in expanding research opportunities. 

Help us protect and restore the Morro Bay estuary!