About

Plant breeder, phenomics researcher, and digital breeding scientist.

I work at the intersection of field breeding, quantitative genetics, high-throughput phenotyping, computer vision, robotics, and environmental prediction.

Current roleResearch Assistant Scientist and Phenomics & Digital Breeding Team Leader, University of Florida Blueberry Breeding & Genomics Lab.

Biography

From field breeding to digital decision support.

Paul Motunrayo Adunola is a plant breeder and Research Assistant Scientist in the University of Florida Blueberry Breeding & Genomics Lab, where he leads the Phenomics and Digital Breeding Team. His research integrates plant breeding, high-throughput phenotyping, computer vision, robotics, quantitative genetics, genomics, and environmental information to improve selection decisions and cultivar development.

His current research includes robotic and image-based blueberry yield phenotyping, computer vision for plant health, cultivar development for robotic harvesting, multi-domain prediction of blueberry yield, genotype-by-environment analysis, target-environment prediction, and breeder-facing recommendation systems.

Paul earned a Ph.D. in Horticultural Science from the University of Florida. His earlier training in plant breeding and crop science spans France, Finland, Nigeria, and the United States. He has also worked in breeding and quantitative genetics roles with KWS, Corteva Agriscience, the International Institute of Tropical Agriculture, and the University of Florida.

He values interdisciplinary collaboration, mentoring, and research that can move from analysis to practical use in breeding programs. Outside research, he enjoys travel, music, small-canvas acrylic painting, cooking, and pickleball.

Experience & education

A breeding perspective shaped across crops, countries, and disciplines.

Experience

2026–present
Current

Research Assistant Scientist

University of Florida · Blueberry Breeding & Genomics Lab

Lead the Phenomics and Digital Breeding Team. Develop research lines in robotics, computer vision, high-throughput phenotyping, multi-domain data integration, target-environment prediction, and decision support for cultivar development.

2024–2026

Postdoctoral Associate

University of Florida

Applied computer vision, AI, robotics, multi-domain prediction, and digital tools to blueberry breeding and phenotyping.

2021–2024

Research Assistant

University of Florida

Blueberry breeding, genomic and phenomic prediction, G×E, field phenotyping, computer vision, gene discovery, and selection.

Earlier research & industry experience

2021 · KWS, Germany
Sugar beet breeding intern — root imaging and trial analysis.

2020 · Corteva Agriscience, France
Maize breeding intern — disease phenotyping and trial analysis.

2020 · University of Florida
Quantitative genetics intern — genomic prediction and stability analysis.

2016–2017 · Federal University of Technology Akure, Nigeria
Graduate research assistant — field trials in maize, soybean, sorghum, and Bambara groundnut.

2012 · International Institute of Tropical Agriculture, Nigeria
Cassava breeding intern — characterization and postharvest evaluation.

Education

2021–2024

Ph.D., Horticultural Science

University of Florida · Gainesville, USA

Optimization of phenomic and genomic selection and assessment of genotype-by-environment interaction in blueberry fruit-quality traits.

2019–2021

M.S., Plant Breeding

UniLaSalle, France · University of Helsinki, Finland

Identification and expression of putative seed lipoxygenase genes in faba bean.

2015–2018

M.S., Crop Management Technology

Federal University of Technology Akure · Nigeria

Marker-assisted introgression of the Opaque-2 gene into popcorn.

2008–2014

B.S., Crop, Soil & Pest Management

Federal University of Technology Akure · Nigeria

Capabilities

Technical and leadership competencies.

01

Digital breeding & phenomics

Computer vision, high-throughput phenotyping, image-based phenotyping, near-infrared spectroscopy, object detection, robotics, and automation.

02

Predictive breeding

Genomic and phenomic prediction, GWAS, QTL mapping, genomic selection, G×E, sparse testing, and target-environment prediction.

03

Statistics & AI/ML

Mixed models, Bayesian analysis, machine learning, regression, ANOVA, multi-environment analysis, stability analysis, and decision support.

04

Breeding & leadership

Selection, phenotyping, field trials, pollination, germplasm management, project leadership, mentoring, stakeholder communication, and research coordination.

Paul Adunola presenting research at a conference

Presentations & teaching

Research presentations in plant phenomics, genomic prediction, G×E, and multi-domain breeding, plus guest teaching in blueberry breeding, genomic selection, R, QTL mapping, and GWAS.

05

Selected recognition

  • 2026 NAPPN / IPPN PhenomX Travel Award
  • 2025 Outstanding Paper Award, The Plant Phenome Journal
  • 2024 Bayer University Mentorship Program
  • 2023–2026 FFAR Fellowship
  • 2022 Best Master's Thesis, University of Helsinki
  • 2019 Erasmus Mundus Plant Breeding Scholarship
06

Leadership & service

Founder and fundraising/budgeting lead for Edu-Kids Foundation; judge for SwampHacks X; former secretary of MANRRS and the UF Plant Science Council.

Service

Edu-Kids Foundation

I founded Edu-Kids Foundation to support the educational and social welfare needs of orphans and vulnerable children in Nigeria. The organization works to reduce barriers to education through student support, food assistance, scholarships, sanitary-hygiene support, counseling, and monitoring.

Visit Edu-Kids Foundation

Contact

Let us connect.

I am interested in collaborations that connect breeding, phenomics, quantitative genetics, environmental prediction, and useful digital tools.