Pi 5 Predictive Crop Yield Model
Raspberry Pi 5 Predictive Crop Yield Model Kit with IoT Sensors & LSTM
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Imagine deploying a Raspberry Pi in a field that not only logs temperature, humidity, soil moisture, and light levels but also learns from past seasons to forecast tomorrow's harvest. This kit transforms that scenario into a working edge AI system—no cloud dependency, no theoretical textbook exercise. You wire real sensors to a Pi 5, stream data onto NVMe storage, and train an LSTM neural network that predicts crop yield from historical patterns. It’s precision agriculture built from first principles.
What You'll Build
You assemble a complete edge intelligence station: a Raspberry Pi 5 with DHT22, capacitive soil moisture probe, and BH1750 lux sensor collecting environmental data at regular intervals. Raw readings are logged to the 512GB NVMe SSD via the M.2 HAT, creating a rich time series. Using TensorFlow or PyTorch, you train a multi-layer LSTM on this dataset to output a yield forecast—ready for validation against real harvest figures.
What You'll Learn
- Train an LSTM network on multivariate time series data from agricultural sensors
- Build an end-to-end IoT data pipeline on Raspberry Pi 5 with NVMe SSD persistence
- Calibrate DHT22, soil moisture, and light sensors for reliable long-term outdoor logging
- Deploy a lightweight inference engine locally—no cloud, no latency, just edge AI
Kit Contents
| Component | Quantity |
|---|---|
| Raspberry Pi 5 8GB | 1 |
| DHT22 | 1 |
| Soil Moisture Sensor | 1 |
| BH1750 Light Sensor | 1 |
| NVMe SSD 512GB | 1 |
| Pi 5 M.2 HAT+ | 1 |
| USB-C PSU | 1 |
| M-M Wires | 15 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
This kit meets the needs of advanced BTech ECE, EEE, and agricultural engineering students working on final-year capstone projects at IITs, NITs, VIT, or BITS. It’s equally suited for Smart India Hackathon teams tackling precision agriculture challenges, ATL Tinkering Lab mentors running IoT+AI workshops, and independent makers who want a real-world time series ML experience without sourcing sensors one by one.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
Scan the QR code to launch the AI companion trained on this exact crop yield predictor; it will walk you through wiring, coding, and model training. You can also reach us over WhatsApp for human assistance within 24 hours.
Where can I source the historical sensor data to train the LSTM?
The AI companion provides a sample dataset of environmental readings with corresponding yield labels so you can start training immediately. You can later replace it with your own collected sensor logs.
Do I need prior deep learning experience to complete this project?
Familiarity with Python and basic neural networks helps, but the companion guides you through setting up TensorFlow, defining the LSTM architecture, and tuning hyperparameters—turning it into a learn-by-building experience.
Can this system run on battery power on a remote farm?
Yes, the Pi 5 with sensors draws under 15W and can run off a solar-charged power bank. The kit includes a stable USB-C PSU for bench development, but field deployment with alternative power is straightforward.
Historical sensor data trains an LSTM on Pi 5 to predict crop yield — combines IoT data collection with time series ML.
What's in this kit
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items