Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE

TL;DR

This study compares the ODYSSEE AI model's ability to identify live oysters from images with human annotators, revealing current limitations in accuracy but potential for improvement with better data and training methods.

Contribution

It provides a comparative analysis of AI and human oyster identification, highlighting the impact of image quality and proposing directions for enhancing model accuracy.

Findings

AI is faster but less accurate than humans in oyster identification.

Image quality affects both human and AI accuracy, with higher quality improving human performance.

Future improvements could significantly enhance AI's predictive capabilities.

Abstract

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. Therefore, they are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, $2.34 \pm 0.61$ h, $4.50 \pm 1.46$ h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63\%) in identifying live oysters compared to experts (74\%) and non-experts (75\%) alike. Image quality was an important factor in determining the accuracy of the model and the annotators. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs. dead oysters.