About Agriculture Intelligence
Per-plant intelligence for tree crops, built on peer-reviewed research
Agriculture Intelligence, Inc. (AgIntel) was founded in 2020 in Gainesville, Florida, to take UF-licensed technology from the research orchard to commercial acres. We build Agroview, which turns aerial imagery into a per-tree inventory, and Agrozoom, which brings aircraft imagery to drone-level detail. Crop insurers and growers use them to see what is actually planted.
2020
founded in Gainesville, FL
5
peer-reviewed papers behind the platform
US 12,008,730
issued U.S. patent, plus Spain and Mexico
2 of 7
U.S. tree-crop insurers under multi-year contract
Leadership
Matthew Donovan
Co-founder & CEO
Two-time founder with more than 25 years in technology and product management, including insurance. Leads Agriculture Intelligence's strategy and its work with crop insurers and growers.
Yiannis Ampatzidis, PhD
Co-founder & Chief Science Officer
Inventor of Agroview, named on its U.S. patent, and author of the peer-reviewed research behind it; co-author of the AgI-GAN research that underpins Agrozoom. Professor at UF/IFAS and a leading voice for the use of AI in agriculture.
The research
Every core capability traces to a peer-reviewed paper, each validated against ground truth: hand counts, lab-analyzed leaf samples, or drone imagery.
- 2019Per-tree detection
99.8% tree detection F-score on 4,931 ground-truthed citrus trees; gap detection at 100% precision and 94.6% recall.
Ampatzidis, Y., & Partel, V. (2019). UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence. Remote Sensing. doi:10.3390/rs11040410
- 2020Agroview at commercial scale
97% tree count accuracy across 175,977 trees on 1,871 acres of commercial citrus, with at least 70% lower data-collection cost and 90% less time than manual counts.
Ampatzidis, Y., Partel, V., & Costa, L. (2020). Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2020.105457
- 2021Nutrient mapping
Eleven leaf nutrients predicted from canopy reflectance, validated against 804 lab-analyzed leaf samples; nitrogen within 3.82% mean error.
Costa, L., Kunwar, S., Ampatzidis, Y., & Albrecht, U. (2021). Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precision Agriculture. doi:10.1007/s11119-021-09864-1
- 2024Consistency over time
Four flights over 22 days on the same blocks: tree counts varied by under 3% (coefficient of variation 1.56–2.63%).
Trentin, C., & Ampatzidis, Y. (2024). Assessing the precision and consistency of Agroview in orchard management: A multi-temporal analysis. Smart Agricultural Technology. doi:10.1016/j.atech.2024.100693
- 2025Agrozoom (AgI-GAN)
Aircraft imagery enhanced from ~15 cm to ~3.9 cm per pixel: tree detection error fell from 36.21% on raw imagery to 4.11%, at about 60% lower cost per acre than drone (UAV) imagery.
Lacerda, C. F., Ampatzidis, Y., de Oliveira Costa Neto, A., & Partel, V. (2025). Cost-efficient high-resolution monitoring for specialty crops using AgI-GAN and AI-driven analytics. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2025.110678
Patents
- United States — US 12,008,730Issued
- Spain — ES2938091Granted
- Mexico — counterpart of the U.S. familyIssued
- Brazil — national phasePending
Recognition & funding
- 2026eMERGE Americas Final 7
- 2025Florida Next Big Bet — Florida Chamber of Commerce
- 2025VCIC Global Finals — Most Backable Company
- 2021ASABE Top 50 Global Product
- 2020UF Innovation of the Year
Non-dilutive funding from USDA SBIR. Selected to present at the 2026 Florida Venture Forum.
Talk to us
Contact · agroview@agintel.ai · LinkedIn · X