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

arXiv: https://arxiv.org/abs/2505.03108 · HTML: https://mlsanity.com/paper/2505.03108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 8 of 8)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [How much real data do we actually need: Analyzing object detection performance using synthetic and real data](https://mlsanity.com/paper/1907.07061/) (2019)
- [OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring](https://mlsanity.com/paper/2209.09395/) (2022)
- [BenthicNet: A global compilation of seafloor images for deep learning applications](https://mlsanity.com/paper/2405.05241/) (2024)
- [ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics](https://mlsanity.com/paper/2409.07003/) (2024)

## Built on by (most cited first, 1 of 1)

- [Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation](https://mlsanity.com/paper/2507.18558/) (2025)

## Abstract (arXiv)

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 ± 0.61 h, 4.50 ± 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.