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

This Saturday (March 14, 2026), I will demonstrate an exciting AI project at Chongqing Optical Valley—the "Smart Lobster Sorting System." Many people's first reaction seeing this project is: "Isn't this just simple image recognition?" But the truth is far more complex than imagined. Today, I want to deeply reveal the technical architecture behind this project and why it represents a new paradigm for AI landing in traditional industries.

Industry Insider: Three Major Pain Points in Aquatic Product Sorting

Before getting involved in this project, I researched the local aquatic product market in Chongqing. Found three fatal problems with traditional lobster sorting:

  1. High labor costs: Skilled sorters earn 8000+ yuan/month with high turnover
  2. Ununified standards: Different masters have huge differences in "freshness" judgment
  3. Efficiency bottleneck: A single person can process at most 200 jin per hour, unable to meet scale needs

These pain points seem simple, but solving them with AI requires cross-domain technology integration.

Technical Architecture Overview

Our AI lobster system adopts a three-layer architecture design:

Layer 1: Edge Computing Layer

  • Hardware: NVIDIA Jetson Orin + industrial camera
  • Function: Real-time image acquisition, preprocessing, preliminary classification
  • Advantage: Latency <50ms, no cloud dependency

Layer 2: AI Model Layer

  • Core model: Improved YOLOv8 + Vision Transformer
  • Training data: 500,000 annotated lobster images (covering different varieties, lighting, angles)
  • Special features: Living detection, freshness assessment, weight prediction

Layer 3: Business Logic Layer

  • OpenClaw integration: Automated workflow orchestration
  • Data dashboard: Real-time monitoring of sorting accuracy and efficiency
  • API interface: Seamless connection with ERP systems

Correcting Perceptions: AI Is Not All-Powerful

Many people think AI can solve all problems, but during actual deployment we encountered unexpected challenges:

  • Lighting changes: Complex lighting in aquatic markets requires extensive data augmentation
  • Occlusion issues: Lobster stacking severely affects recognition
  • Variety differences: Lobsters from different origins have huge appearance differences

Our solution is adopting a "multi-modal fusion" strategy, combining vision, weight sensors, and historical data, rather than relying solely on image recognition.

Future Predictions: Golden Decade for AI + Traditional Industries

This project at Chongqing Optical Valley is just the beginning. I believe in the next 5 years, AI will explode in the following traditional fields:

  1. Agriculture: Crop pest identification, yield prediction
  2. Manufacturing: Defect detection, quality control
  3. Services: Personalized recommendations, intelligent customer service

The key is finding real pain points, not doing AI for AI's sake.

Complete OpenClaw Installation Video Tutorial: Click to view BiliBili video

AI Programming Course Poster

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Offline Event Registration: 30 yuan to participate, then contact my information above, note: Optical Valley

About event fees: The event charges because the venue requires fees. If a free venue can be provided, my events can also be free.

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Two courses included:

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