Automated Phenotyping of Perennial Food Crops

Jan 15, 2023 · 1 min read
project

Overview

This project develops automated phenotyping systems using computer vision and machine learning to accelerate breeding programs for perennial food crops, with a focus on avocado.

Funding: ~$86,000 USDA NIFA subaward (2023-2024) Role: Co-Investigator

The Problem

Traditional phenotyping of perennial crops like avocado is labor-intensive and time-consuming, limiting the scale and speed of breeding programs needed to develop climate-resilient varieties.

Our Approach

  • Computer Vision: Automated image analysis for trait extraction
  • High-Throughput Phenotyping: Scalable data collection pipelines
  • Machine Learning Models: Trait prediction and selection optimization
  • Integration: Connecting phenotypic data with breeding decision-support systems

Key Outcomes

  • High-throughput phenotyping using computer vision
  • Automated data collection and analysis pipelines
  • Machine learning models for trait prediction
  • Integration with breeding program workflows

Impact

Accelerating selection cycles and improving precision in trait evaluation for perennial crop breeding, supporting the development of climate-resilient avocado varieties.

Machine Learning Computer Vision Agriculture AI Crops
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.