CV Framework

Open-source computer vision for agriculture and conservation

Status: Active Development Target Users: Non-expert researchers, agricultural scientists, conservationists Philosophy: Democratizing AI/ML for Real-World Applications

Overview

We are developing an open-source, accessible computer vision framework that enables researchers without deep machine learning expertise to rapidly load data, train models, and deploy solutions for real-world problems.

The Problem

Barriers to Computer Vision Adoption

Technical Expertise Gap:

  • Requires programming skills (Python, deep learning frameworks)
  • Complex data preprocessing and augmentation
  • Model architecture selection and hyperparameter tuning

Domain-Specific Challenges:

  • Agricultural datasets differ from ImageNet
  • Limited labeled training data
  • Field deployment constraints

Our Solution

Design Principles

Accessibility First

  • Simple configuration files (YAML/JSON) instead of code
  • Sensible defaults for common use cases
  • Automatic data preprocessing and augmentation

Rapid Prototyping

  • Load data -> Train model -> Deploy in hours, not weeks
  • Built-in visualization of results
  • Real-time training monitoring

Production-Ready

  • Export to edge deployment formats (TensorFlow Lite, ONNX)
  • REST API deployment
  • Inference optimization

Use Cases

1. Sturgeon Sex Determination

  • Classify fish images as male/female
  • Impact: 30-50% reduction in operational costs

2. Plant Disease Detection

  • Identify crop diseases from leaf images
  • Impact: Early intervention, reduced pesticide use

3. Wildlife Monitoring

  • Species identification from camera traps
  • Impact: Scalable wildlife surveys

4. Agricultural Phenotyping

  • Measure plant traits from images
  • Impact: Faster crop improvement cycles

Technical Stack

  • Backend: PyTorch, TensorFlow, FastAPI
  • Data Processing: Albumentations, OpenCV, NumPy/Pandas
  • Deployment: Docker, TensorFlow Lite, ONNX
  • MLOps: MLflow, DVC, GitHub Actions

Performance

  • Matches or exceeds custom implementations
  • 90%+ accuracy on domain-specific tasks
  • <1 week from data to deployed model
  • Works on limited datasets (100s of images with augmentation)

Get Involved

For Beta Testing: Contact esolares [at] ucsd [dot] edu with your use case

Code Repository (coming soon): github.com/ESB-AI-Lab/cv-framework