Open-Source Computer Vision Framework

Jun 1, 2024 · 1 min read
project

Overview

Developing open-source computer vision frameworks that enable researchers and practitioners without deep ML expertise to deploy powerful image analysis tools for agricultural and biological applications.

Goals

  • Accessibility: User-friendly interfaces for non-programmers
  • Flexibility: Adaptable to diverse species and use cases
  • Reproducibility: Standardized pipelines for scientific rigor
  • Community: Building collaborative development ecosystem

Applications

  • Species identification and classification
  • Phenotype measurement and tracking
  • Quality assessment in agriculture
  • Conservation monitoring

Technology Stack

  • PyTorch and TensorFlow backends
  • Pre-trained models for transfer learning
  • Cloud deployment options
  • Mobile-friendly inference
Machine Learning Computer Vision Open Source Agriculture
Edwin Solares
Authors
Edwin Solares (he/him)
Lecturer in Computer Science & Data Science
I am a computational biologist and data scientist bridging artificial intelligence, evolutionary genomics, and climate-resilient agriculture. My research leverages cutting-edge machine learning and bioinformatics to address global food security challenges in the face of rapid climate change. With publications in high-impact journals including Nature Plants, PNAS, and Genome Research (h-index: 7), I develop tools and methods that advance both computational science and real-world applications.