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Case studies and product thinking

Five short pieces on what actually matters in plant counting: faster collection, where manual sampling breaks down, how model performance changes across cameras and datasets, and why density alone misses the real stand-quality story.

In this series

Field workflowApr 22, 2026

Agronomists Don't Use Drones

Why overlap-heavy drone mapping is too slow for everyday scouting, and how PlantCounts uses single-photo row-based scaling instead.

Single-photo capture

Validation

Sampling biasApr 26, 2026

Aren't Manual Counts Enough?

A practical look at why manual counts still help, but often fail to represent the true stand across a field.

Manual counts

Sampling

Camera alignment visual from the PlantCounts Multi-Res Canola case study.

Canola case studyApr 27, 2026

Case Study 1 - Multi-Res Canola

How PlantCounts trains across multiple camera resolutions so agronomists can keep a safer altitude buffer without giving up count quality.

Canola

GSD robustness

Dataset generalizationApr 28, 2026

Case Study 2 - USask Canola Dataset

What changed after PlantCounts brought a public USask canola dataset into training to improve weed suppression and out-of-domain performance.

Canola

Public dataset

Gap detection cover image from the PlantCounts article Row Gaps and Stand Uniformity.

Stand qualityApr 29, 2026

Row Gaps & Stand Uniformity

Why stand density alone is not enough, and how PlantCounts turns spacing error and row gaps into measurable field-level signals.

Uniformity

Gap analysis

PlantCounts

Reliable stand data without hours of manual counting.

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