# Building in Public: Week 1 - Our Tech Stack Decision
Welcome to our first "Building in Public" update. We're sharing our development journey transparently—the wins, the challenges, and the decisions that shape AgriLink.
This week: **choosing our core technology stack.**
## The Big Decisions
**1. Satellite Imagery: SkySat + Sentinel-2**
We evaluated five satellite providers. It came down to resolution vs. cost vs. revisit frequency.
**Winner: SkySat** (0.5m resolution, daily revisits) for high-priority fields, with **Sentinel-2** (10m resolution, free, every 5 days) as the base layer for all farms.
Why this combo? SkySat gives us the detail to catch early stress. Sentinel-2 makes it affordable to monitor huge areas. Best of both worlds.
**2. AI Model: Custom Fine-Tuned Vision Transformer**
We tested pre-trained models (ResNet, EfficientNet) and decided to fine-tune a Vision Transformer (ViT) on agricultural data. Early results are promising—93% accuracy detecting stress vs. 78% with off-the-shelf models.
The catch? Training took 6 days on 40,000 labeled field images. Worth it.
**3. Sensor Integration: MQTT + REST API**
We're supporting both protocols so farmers can use existing sensors or our recommended models. LoRaWAN for long-range connectivity in remote fields.
**4. Frontend: React + Tailwind CSS**
Fast, responsive, mobile-first. Farmers check their phones in the field—our UI has to work perfectly on a 5-inch screen in bright sunlight.
## Challenges This Week
**Problem 1: Cloud Cover**
Sentinel-2 gets blocked by clouds 40% of the time in northern Europe during winter. Our solution: temporal interpolation using past data + weather forecasts to estimate field conditions when satellites can't see through.
**Problem 2: Data Pipeline Latency**
Raw satellite images take 2-4 hours to process and deliver insights. We want this under 30 minutes. Our fix: pre-processing pipelines with AWS Lambda and edge caching. Still optimizing.
**Problem 3: Sensor Calibration**
Cheap soil sensors drift over time. We're building auto-calibration algorithms that compare sensor data to satellite moisture estimates (from radar). Tricky but doable.
## What's Next (Week 2)
- Finalizing our AI alert logic (when to notify farmers vs. when to stay quiet)
- Building the first version of our SMS/email notification system
- Interviewing 5 pilot farmers to validate our dashboard mockups
## We Want Your Feedback
If you're a farmer, agronomist, or just interested: what features matter most to you? What would you check every morning? Reply to this post or email us at hello@agril.ink.
**Pre-register to get next week's update:** [Join the waitlist](#)
— The AgriLink Engineering Team