From Satellite Pixels to Farmer Decisions: What Building FieldLens Taught Me

Description and use

FieldLens turns free satellite data into actionable, privacy‑preserving advisories for small growers, reducing manual field checks and enabling cost‑effective monitoring.

Problem

Farmers in Guangdong need actionable insights from satellite data but lack tools to interpret it.

Outcome

FieldLens turns free satellite data into actionable, privacy‑preserving advisories for small growers, reducing manual field checks and enabling cost‑effective monitoring.

Workflow

• Draw a polygon on the map or use a sample orchard.
• Pull Sentinel‑2 NDVI time series via Google Earth Engine and composite into vigor zones.
• Run a local Gemma model via Ollama to generate a plain‑language advisory report in Chinese.
• Present the report to the farmer; pricing is 199 RMB/plot/year.
• Iterate based on field validation scheduled for November.

Evidence

Satellite data could answer all of this — Sentinel‑2 flies over every five days, free for anyone to use.

Source

A local Gemma model via Ollama writes the advisory report. This was a deliberate choice, not a gimmick: grower data never leaves the machine, there's no per-report API bill, and it works where connectivity is flaky.

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During research I found that NDVI ≥ 0.70 is a drone canopy-pixel reference, not a Sentinel‑2 block-average threshold. Mixing those up is exactly how ag‑tech demos lie to farmers.

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Every screenshot and demo polygon is a sample geometry with a visible "Sample" badge. I never drew a real orchard boundary in any public material.

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