The global seafood industry faces inefficiencies in quality assessment, particularly for high-value products like abalone, where manual inspection leads to unnecessary harvesting, wasted resources, and ecological harm. Current methods lack precision, causing financial losses for farms and unsustainable practices. An automated, non-invasive solution could transform this niche market by improving accuracy and sustainability.

How It Works

One way to address this challenge is by developing AI models that analyze abalone (and eventually other seafood) through images or video feeds. The system could:

• Detect and count abalone in underwater footage or farm enclosures.
• Estimate size and weight using computer vision, such as measuring shell dimensions.
• Assess harvest readiness by analyzing visual cues like shell texture, color, or growth patterns.
• Recommend actions, flagging specimens to harvest or release to optimize yield and sustainability.

The technology could be deployed via mobile apps for farmers to upload images or integrated with underwater cameras for continuous monitoring. This approach could reduce labor costs, minimize waste, and improve harvest accuracy.

Potential Impact and Execution

Abalone farms stand to benefit significantly from higher profits and compliance with sustainability standards. Chefs and retailers could gain access to better-quality, sustainably sourced seafood, while marine ecosystems would benefit from reduced unnecessary harvesting.

An MVP could start with a basic image-based model for size and weight estimation, using open-source frameworks like TensorFlow or PyTorch. Partnering with a few farms for pilot testing would help refine the system. Data collection could involve crowdsourcing labeled images from farms or divers, possibly incentivized with free analysis services. Scaling could include adding maturity prediction and expanding to other seafood like lobsters or scallops.

Comparison with Existing Solutions

Unlike academic projects that focus solely on research, this idea aims to operationalize AI for daily farm use. While some companies like Aquabyte and Tidal offer AI solutions for seafood farming, they either target different species (e.g., salmon) or have a broader ecological focus. This proposal would specifically address abalone farmers' needs, such as shell-based metrics and harvest optimization.

By starting with a high-value, data-scarce market like abalone, this approach leverages existing AI advancements to solve a pressing industry problem. Its scalability and alignment with sustainability trends position it for long-term impact.