ESP32 Houseplant Monitor Kit with LSTM Yield Forecast & LoRa
Indoor Houseplant Monitor Pro Kit with ESP32 - NDVI-Based LSTM Yield Forecast Over LoRa
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Transform your houseplant care from guesswork into data-driven cultivation. This kit equips you to build a solar-powered monitor that captures ambient temperature, humidity, soil moisture at three depths, rainfall, and 18-channel spectral reflectance (NDVI) - then feeds it all into an on-device LSTM neural network to predict weekly foliage yield. The forecast is transmitted kilometres away over LoRa, so you can check plant health from anywhere without cloud costs.
What You'll Build
A fully autonomous indoor plant monitor that logs multi-sensor data to microSD, runs a TinyML model on the ESP32-S3, and sends yield predictions via RA-02 LoRa to a distant receiver. The waterproof enclosure and 6V solar panel keep it running indefinitely, making it ideal for agricultural research, B.Tech capstone projects, or serious hobbyists growing high-value indoor plants like vanilla orchids or microgreens.
What You'll Learn
- Acquire and fuse data from DHT22, capacitive soil moisture sensors, AS7265x 18-channel spectral sensor, rain sensor, and DS3231 RTC.
- Train and quantize a lightweight LSTM model for time-series prediction of plant yield, then deploy it on the ESP32-S3 using TensorFlow Lite Micro.
- Configure and run a LoRaWAN stack on the RA-02 module with 433MHz antenna to send inference results over kilometres.
- Design a solar-rechargeable power system using TP4056 charging, 18650 cells, and LM2596 buck converter for uninterrupted outdoor-like indoor operation.
Kit Contents
| Component | Quantity |
|---|---|
| ESP32-S3 Dev Board | 1 |
| DHT22 | 2 |
| Soil Moisture Sensor | 3 |
| AS7265x Spectral | 1 |
| Rain Sensor | 1 |
| DS3231 RTC | 1 |
| MicroSD Module | 1 |
| RA-02 LoRa | 1 |
| 433MHz Antenna | 1 |
| Solar Panel 6V 2W | 1 |
| TP4056 Module | 1 |
| 18650 Cell | 2 |
| LM2596 Buck Converter | 1 |
| 100nF Caps | 15 |
| PCB Prototype Board | 3 |
| Waterproof Enclosure | 1 |
| Soldering Iron | 1 |
| Solder Wire | 1 |
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
Designed for B.Tech ECE/EEE students building agritech capstone projects, Smart India Hackathon participants tackling precision agriculture, and researchers at IITs, NITs, VIT, or BITS Pilani experimenting with edge AI. It's equally suited for ATL Tinkering Lab mentors wanting an advanced IoT courseware and serious urban gardeners monitoring rare indoor crop varieties.
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 included to chat with the AI companion that has seen every step of this exact project, or drop a WhatsApp message to our support team with a photo of your setup - we respond within hours.
Do I need prior machine learning experience to run the LSTM model?
No. The kit includes pre-trained model weights and a step-by-step workflow to quantize and flash the TensorFlow Lite model onto the ESP32-S3. You'll learn the end-to-end pipeline without needing a deep ML background.
Can I adapt this setup for a small indoor greenhouse instead of a single pot?
Absolutely. The three soil moisture sensors and dual DHT22 can cover multiple plants or zones, and the spectral sensor's wide field of view captures canopy-level reflectance for several square feet. The AI companion includes guidance on scaling.
How accurate is the weekly yield forecast?
The on-device LSTM model has been trained on curated houseplant datasets and typically achieves a mean absolute error below 12% for leaf area yield when environmental conditions stay within the training envelope. You can retrain it with your own data for improved accuracy over time.
Houseplant - Temperature, humidity, soil moisture, NDVI and rainfall fed into LSTM model. Weekly yield forecast via LoRa.
What's in this kit
Choose your assembly option:
- Soldering Kit - 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
- Breadboard Combo - 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.
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