Coral Doctor is an machine learning based technology that works to preserve and safeguard coral reefs for future generations. We are a hybrid research and social media platform, that brings together ocean enthusiasts, scientists, and government officials together to serve a common goal and save one of our environments most assets. Coral reefs contribute to over 8 trillion dollars of the world's GDP annually, and Coral Doctor ensures we protect this resources over 200 million individuals depend on. Users log onto our platform and upload underwater coral photography to share with other enthusiasts, and our propriety software processes the image. Then, our machine learning algorithms analyze the data, and determine if the coral is in a healthy or sick condition. This can help identify potential treatment or recovery options to government or institution bodies. Through leveraging diving enthusiasts' crowdsourcing, government organizations, and NGO environmental protection agencies, Coral Doctor will have an immense effect on ensuring we preserve the oceans' greatest assets today, for the generations of tomorrow.
Our website, Coral Doctor, allows users to analyze images for coral bleach percentage, which indicates a diseased coral.
Our app enables a community of divers and researchers to share their images and analyze coral images for signs of disease, so we may better protect the reefs of our oceans.
We used Hawaiian Ko'a Card Coral Health Assessments data to help clasify the bleaching level of coral in in the ocean based on factors like depth and a color score based on a coral health assessment tool. The tool devloped by the Coral Reef Ecology Lab of the Hawaii Institute of Marine Biology helps identify the color of coral and assign it a score. This color data is extremely powerful in understanding the bleach percentage of the coral.
The above figure displays the count of coral at various color scores with indication of bleaching level. After plotting this data, we employed Ordinal Probit Regression and Logistic Regression to predict coral bleaching levels with ~75% accuracy.
We had some issues implementing other classification algorithms,such as a Gradient-Boosted Tree. We might have been overfitting our data because of a smaller dataset or because the dataset had a low amount of features. The dataset we used was limited, as the other larger data sets were private. In the future, we would look into analyzing a much larger dataset with more features, so we can target a higher accuracy but avoid overfitting.