Neil Olson, M.S.
Quantitative Agronomist & Data Scientist
Leveraging UAV multispectral remote sensing, spatial analytics, and machine learning to decode complex plant systems. Published author in Agronomy Journal with industry experience in field variety trials, predictive disease modeling, and production data infrastructure.
Professional Profile
Rooted in Biology, Driven by Quantitative Rigor
My academic and professional trajectory spans the entire spectrum of plant biology and modern computing: from managing commercial sugarbeet disease trials and operating research UAV flights, to training convolutional vision models and architecting cloud data pipelines.
Whether processing calibrated 5-band multispectral imagery to model canopy dynamics, predicting root rot pathogen pressure from 20-year longitudinal datasets, or building production software at Dakota AI, my focus is translating complex biological variance into reproducible, actionable data.
2024
Agronomy Journal Publication
4.00
Data Science & B.S. GPA
Dual M.S.
Plant Science & Data Science
Part 107
FAA Certified Remote Pilot
Education
M.S. in Data Science Department GPA: 4.00
North Dakota State University (NDSU)
Advanced graduate program focusing on statistical machine learning, enterprise data engineering, cloud computing, and relational database systems. Maintained a straight-A record (4.00 Department GPA) across all data science, machine learning, and computer science curricula.
Key Graduate Coursework
M.S. in Plant Sciences GPA: 3.35
North Dakota State University (NDSU)
Thesis: "Canopy closure, yield, and quality under heterogeneous plant spacing in sunflower."
Advised in collaboration with USDA-ARS and Texas A&M AgriLife. Formalized UAS multispectral imaging methods for high-throughput canopy phenotyping. Published in Agronomy Journal (2024).
B.S. in Crop and Weed Sciences GPA: 4.00
North Dakota State University | Summa Cum Laude
Major: Agronomy. Minors: Botany and Biological Sciences. Ranked at top of graduating class with unblemished 4.00 GPA across all scientific disciplines.
A.A. & A.S. in Liberal Arts Transfer GPA: 3.77
Bismarck State College
Emphasis in Philosophy and formal logic. Provided foundational systems thinking and philosophical rigor that informs my publications on information theory and simulation models.
Experience
Founder & Lead Data Scientist
Dakota AI LLC
Specialized technical consultancy developing artificial intelligence, custom machine learning workflows, and data solutions for agriculture and commercial partners across the Northern Plains.
• Engineered full-stack production deployments and automated data pipelines.
• Consulted on computer vision applications, spatial data handling, and custom web platforms.
Production Agronomist
Southern Minnesota Beet Sugar Cooperative
Directed operational sugarbeet variety trials (OVT) and disease nurseries across regional growing areas.
• Analyzed longitudinal agricultural datasets spanning 20+ years to build predictive disease risk models for Rhizoctonia root rot.
• Supervised field scouting personnel, coordinated seed treatments, and presented trial results to agricultural staff and growers.
Graduate Research Assistant
USDA-ARS & North Dakota State University
Led field experimentation and UAS multispectral imaging operations evaluating spatial heterogeneity in sunflower.
• Planned and executed sUAS flights with MicaSense RedEdge MX and DJI Zenmuse sensors.
• Processed raw photogrammetry in Agisoft Metashape and Pix4D, computing EVCI and NDVI vegetation indices.
• Performed complex mixed-model ANOVA in SAS (PROC MIXED, GLM) on Randomized Complete Block Designs (RCBD).
Research & Technical Work
Peer-reviewed agronomic research, high-throughput UAS phenotyping, and computer vision workflows.
Canopy closure, yield, and quality under heterogeneous plant spacing in sunflower
Olson, N. A., Trostle, C., Meyer, R., & Hulke, B. S. (2024). Agronomy Journal, 116(5), 2412–2426.
Investigated the impact of non-uniform within-row plant spacing (skips, doubles, and clusters) on sunflower canopy development, seed yield, and oil concentration. Integrated high-resolution sUAS multispectral imaging with Enhanced Vegetation Coloration Index (EVCI) modeling to quantify canopy closure over time without destructive sampling.
UAS Multispectral Orthomosaic
UAS High-Throughput Phenotyping & EVCI
Quantified canopy growth kinetics across field plots using calibrated 5-band MicaSense multispectral imagery. Developed raster masking pipelines in ArcGIS Pro to eliminate soil background reflectance and model temporal canopy dynamics.
ArcGIS Feature Segmentation
Spatial Plant Counting & Segmentation
Engineered an object-based feature extraction methodology in ArcGIS Pro using Segment Mean Shift and NDVI thresholding to isolate individual vegetative sunflower seedlings (V4–V8), enabling automated plot-level stand counts.
Computer Vision Classification
Automated Bloom Stage Detection
Trained computer vision models to identify and classify reproductive bloom stages in sunflower breeding nurseries. Replaced labor-intensive manual scoring with automated image segmentation to accelerate genetic evaluation.
Technical Competencies
Statistical Computing & ML
- Python (Scikit-Learn, PyTorch, Pandas, NumPy)
- R (Statistical Computing, Tidyverse)
- SAS (PROC MIXED, GLM, ANOVA, ANCOVA)
- Predictive Modeling & Regression Diagnostics
Geospatial & Remote Sensing
- ArcGIS Pro, QGIS & Spatial Join Analysis
- Agisoft Metashape & Pix4D Photogrammetry
- MicaSense RedEdge MX & DJI Zenmuse Sensors
- NDVI, EVCI & Vegetation Index Modeling
Data Engineering & Systems
- Relational Databases & SQL (Schema Design)
- Data Ingestion & ETL Pipeline Lifecycle
- Cloud Computing & Virtualization
- Full-Stack Web Development & API Integration
Agronomy & Field Research
- Randomized Complete Block Design (RCBD)
- Official Variety Trials (OVT) Execution
- High-Throughput Canopy Phenotyping
- Plant Pathology (Rhizoctonia, Cercospora)
Certifications
FAA Part 107 Remote Pilot | Commercial Small Unmanned Aircraft Systems (sUAS) Operations
Commercial Ventures & Platforms
Production software, consulting engagements, and technical education platforms.
Dakota AI LLC
Specialized data science and AI consultancy bringing modern machine learning, computer vision, and data engineering to agribusiness and regional enterprises.
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Educational platform combining formal botanical science with digital diagnostics to provide actionable care guides and physiological insight for bonsai cultivation.
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Custom web platform developed for client FonicTonic LLC, featuring bespoke user experience design for music theory education and customer conversion.
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Available for data science consulting, research collaborations, agricultural analytics inquiries, and technical advisory.
neil@dakotaai.us