Chongqing Optical Valley AI Lobster Sorting System: 0 to 1 Practical Tutorial

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Hello everyone, this is programmer Wan Feng actively working on various AI projects.

This Saturday (2026.3.14), I will demonstrate a super cool project at Chongqing Optical Valley—the AI Lobster Sorting System! This isn't ordinary sorting, but an intelligent identification system built with OpenClaw that can automatically determine lobster freshness, size, and variety.

Why Build an AI Lobster Sorting System?

Honestly, when I first heard the "on-site lobster installation" requirement, I was confused too. But thinking carefully, this is actually an excellent AI application scenario:

lobster markets process thousands of lobsters daily
Manual sorting is inefficient, high-cost, and error-prone
Freshness judgment requires experience, beginners find it difficult to master

System Architecture Preview

Our AI lobster sorting system includes three core modules:

Image Acquisition Module: HD camera real-time lobster photography
AI Identification Module: Computer vision model based on OpenClaw
Sorting Execution Module: Robotic arm automatic classification

Step 1: Environment Preparation

First install OpenClaw and related dependencies:

1	pip install openclaw opencv-python torch torchvision

Step 2: Data Collection and Labeling

At Chongqing Optical Valley, we took 500+ photos of lobsters in different conditions with our phones, including:

Fresh live lobsters (bright shells, full vitality)
Semi-fresh lobsters (dull shells, slow movement)
Unfresh lobsters (blackened shells, barely moving)

Step 3: Model Training

Use OpenClaw's built-in tools for rapid model training:

from openclaw.vision import ImageClassifier

# Create classifier
classifier = ImageClassifier(
    classes=['Fresh', 'Semi-fresh', 'Unfresh'],
    model_type='efficientnet_b0'
)

# Train model
classifier.train(
    data_dir='./lobster_data',
    epochs=50,
    batch_size=32
)

Step 4: Deploy to Site

Deploy the trained model to on-site edge computing devices, combined with cameras and robotic arms to achieve real-time sorting.

Pitfall Guide

Lighting issues: On-site lighting varies greatly, data augmentation is needed
Angle issues: Lobsters have various placement angles, multi-angle shooting required
Occlusion issues: Stacked lobsters cause occlusion, 3D vision assistance needed