This case study is limited to information Syngenta has already made public in its own materials. No confidential, proprietary, or client-specific details are included beyond what Syngenta has published — consistent with AgCision's commitment to client confidentiality on every engagement

Every season, Syngenta plants and evaluates more than 100 million corn seeds across roughly 300 locations and over one million research plots in North America — each plot about the size of a pickup truck, each containing a unique batch of genetics.
Before this project, the industry standard for evaluating germination and early plant health — known as "stand counting" — was a walk-by visual assessment: a person walking each plot, eyeballing the stand, and recording it by hand. It worked, but it was slow, expensive (estimated at over half a million dollars a year in labor alone), and limited by what the human eye could reliably distinguish. Small but genetically meaningful differences in stand quality were easy to miss, and getting an evaluation crew to the more remote research locations could mean a five-hour round trip for a single day's data.
With standability directly tied to how a new hybrid's genetics are judged for advancement, the accuracy of this single measurement had outsized influence on years of downstream breeding decisions.

I led the development of what became the Plant Stand Analyzer (PSA) — a ground-based machine combining an array of laser sensors with precision RTK-GPS positioning to detect and geo-reference individual corn plants to within 10 cm of accuracy, while moving across a field at up to 10 mph. I reported into Syngenta's Automation and Engineering team under Giru Dhanasekaran, with Judd Maxwell, Syngenta's North America Market Segment Lead, as the project's key stakeholder representing the research/breeding side of the business.
This wasn't a lab prototype that got handed off — it was engineered through repeated field seasons of real-world failure and iteration: welds snapped, frames fatigued, uneven ground caused problems no bench test had revealed. Each issue meant taking the machine out of the field, diagnosing what actually broke under real conditions, and rebuilding it to hold up the next season. That loop — deploy, fail, diagnose, rebuild — is what turned an interesting sensor concept into a piece of equipment reliable enough to run unsupervised across a continent's worth of test plots, season after season.

• Scaled from roughly 9,000 plots counted in its first full season (2019) to over one million plots counted every year since
• Has counted nearly one billion individual corn plants over six years of deployment
• Multiplied a single employee's effective output by an estimated 40:1 compared to manual walk-by counting
• Proved its value in an unplanned way in spring 2020, when the PSA — still mid-development — was pressed into early service to keep evaluation crews out of shared vehicles and reduce field-team exposure during the pandemic
• Became a foundation for further innovation: current thinking within Syngenta looks at combining PSA data with drone imagery and precision-planter data to extend the information gathered on each individual seed's performance, not just whether it emerged.

This case study describes work disclosed publicly by Syngenta Crop Protection in international patent application WO 2025/221857 A1. It is presented only at the level of detail contained in that published filing.

In sweet corn breeding, advancement decisions live or die on the quality of phenotype data — and that data is expensive to get. Ears are assessed when they reach maturity, roughly 21 days after pollination, and the window is unforgiving: ideally an entire trial location is harvested in a day or two, because if harvest drags out, harvest date itself becomes a confounding variable in the results.

The long-standing way to work inside that window was manual and thin. A person walks into a plot, selects perhaps five "typical" ears at random, takes a mix of objective measurements and subjective calls on them, and extrapolates that handful to the whole plot. It's fast, but it's fragile: small samples are noisy, larger samples cost labor and time the window doesn't allow, and subjective assessments introduce selection and estimation bias. Breeders were being asked to make high-stakes advancement calls on data that was both sparse and partly opinion.

I conceived and led the development of a different approach: capture the phenotype data during harvest itself, instead of treating measurement as a separate manual step.

The core idea was to modify existing harvest machinery so that it gathers precise optical, mass, and positional information on the ears as they are collected — while keeping the equipment transportable and easy to deploy across a research trialing network. Each measurement is automatically tied to precision GPS coordinates and plot barcode information, so every data point is associated with the correct plot with no manual tracking and no transcription step.

The effect is to break the accuracy-versus-labor tradeoff that constrained the old method. Sample size scales up from a handful of ears toward the whole plot; objective quantitative measurement replaces eyeballing; and marketability traits can be assessed with enough confidence to advance the strongest plants earlier in the breeding pipeline. I am the first-named inventor on the resulting patent, working with a cross-functional team of engineers, mechanics, and system integrators as co-inventors.

The immediate, tangible outcome is a protected invention on the public record. International patent application WO 2025/221857 A1, "Method and System for Harvesting a Crop," was published on 23 October 2025 under the Patent Cooperation Treaty, carrying a priority date of April 2024. The assignee is Syngenta Crop Protection, and the filing names six inventors — with me first among them. Anyone can retrieve the full published document and verify the concept, the claims, and the inventorship directly.

Because it was filed as an international (PCT) application rather than a single-country filing, the invention retains the option to pursue protection across the world's major agricultural markets — a reflection of the value placed on solving the problem thoroughly rather than quickly.

Beyond the IP asset itself, the filing documents what the invention changes in practice. The established method leaned on roughly five hand-selected ears and a blend of objective measurement and subjective judgment; the disclosed system replaces that with objective, quantitative measurement at whole-plot scale, captured as the ears are harvested and bound automatically to the correct plot. The downstream consequence, as the filing describes it, is higher-quality and higher-volume trialing data with less bias built in — data that breeders can rely on with more confidence when deciding which plants to advance, and that lets the most marketable material move forward in the pipeline earlier than the old approach allowed.