Board renders

Carrier Board v1

Designed in EasyEDA. Manufactured and assembled by JLCPCB. 2-layer FR4, approximately 100 × 80 mm. Gerbers, BOM with LCSC part numbers, and PickAndPlace file included in the download.

Sankhya Intelligence Carrier Board v1 — front 3D render showing terminal blocks, USB-A port, MAX485, LM2596S buck converter
Sankhya Intelligence Carrier Board v1 — back showing Waveshare ESP32-S3-Nano socket footprint
Sankhya Intelligence Carrier Board v1 — PCB layout routing view in EasyEDA
Sankhya Intelligence Carrier Board v1 — full schematic EasyEDA REV 1.0 showing all subsystems

Interactive component map

What's on the board — and why.

Click any highlighted component to learn what it does and why it was chosen. Every part was selected for unattended field deployment in an agricultural environment.

TB_1 · TB_2 · TB_3 RS-485 Bus (A / B / GND) SEN 12V out USB-A Power only ESP32 socket MAX485 RS-485 transceiver SM712 TVS R5 · 120Ω Q1 sensor pwr Q2 USB pwr R1 · R2 · R3 · R4 LM2596S 12V → 5V 33μH inductor 220μF C1 220μF C2 INA219 batt. monitor 12V DC in SS34 diode 12V ← click any component →
Click any component on the board to learn what it does and why it was chosen for unattended field deployment.

Diagram is a schematic representation. For exact component placement see PCB layout tab above.

Enclosure planning

How big is the assembled unit?

The PCB is compact but the battery and 4G dongle add significant volume. Plan your weatherproof enclosure before ordering. We use a 200 × 155 × 80 mm waterproof ABS enclosure with cable glands, sealed with silicone sealant.

Carrier Board ~100 × 80 mm ESP32-S3-Nano 4G LTE Dongle ~105 × 35 mm LiFePO₄ Battery ~150 × 60 × 35 mm 12.8V / 6000 mAh MINIMUM ENCLOSURE ≥ 200 mm wide ≥ 155 mm We use: 200 × 155 × 80 mm ABS weatherproof enclosure + cable glands + silicone Depth: ≥ 80 mm (barrel jack + wiring + cable glands) PCB / fixed Battery / dongle Enclosure boundary

Pin assignments

GPIO — locked by PCB routing

These are fixed by the copper traces. Your firmware must use these exact GPIO numbers — or use flash.sankhyafarms.com which generates correct firmware automatically for your sensor selection.

FunctionGPIOArduino labelConnected to
RS-485 RXGPIO44D0MAX485 RO — Receive Output
RS-485 TXGPIO43D1MAX485 DI — Data Input
RS-485 Direction (DE/RE)GPIO5D2MAX485 DE + RE tied together
Sensor 12V switchGPIO18D9Q1 gate — switches 12V to soil sensors
USB-A 5V switchGPIO17D8Q2 gate — switches 5V to USB-A port
INA219 SDAGPIO11A4Battery monitor I²C data
INA219 SCLGPIO12A5Battery monitor I²C clock

Platform capabilities

What one node can do.

The board is the edge. The intelligence lives server-side on Cloudflare's global edge and compounds with every season of data.

📡
Up to 3 sensors per node
Three RS-485 terminal blocks wired in parallel — daisy-chain sensors without splicing. Each sensor has its own Modbus slave ID. Mix soil EC/pH/moisture, ambient temperature, or any RS-485 Modbus device. See compatible sensor list →
🌐
WiFi mesh — one SIM, whole orchard
One 4G LTE dongle in one node creates a WiFi hotspot. A TP-Link repeater extends coverage across all irrigation zones. Every other node connects as a WiFi client — one SIM card, one data plan, covers the whole orchard.
☀️
Solar + LiFePO₄ — 15–20 day reserve
20W solar panel charges a 12.8V LiFePO₄ battery with internal BMS. No separate charge controller needed. Deep sleep between hourly reading cycles provides 15–20 days of monsoon autonomy with zero sun exposure.
🔄
Over-the-air firmware updates
Nodes query a Cloudflare Worker on each wake cycle. Updates arrive as signed presigned URLs and flash via esp_https_ota() with automatic rollback on failure. Deployed nodes never need physical access for firmware updates.
🌳
Per-tree AI agronomic intelligence
Sensor data feeds the Sankhya Intelligence platform — a dual-model AI stack (Haiku for fast auditing, Sonnet for deep analysis) generating per-tree fertigation prescriptions from longitudinal soil history, not generic crop tables.
⚡
Browser-based firmware flashing
No IDE, no USB drivers. flash.sankhyafarms.com uses the Web Serial API — plug in via USB-C, select your sensor configuration from the dropdown, and flash in under a minute. Works in Chrome on any operating system.

Ordering guide — JLCPCB

How to order your own boards.

The download contains everything JLCPCB needs for a bare PCB or fully assembled (PCBA) order. A video walkthrough is coming — follow these steps in the meantime.

01

Download and extract the zip

Download Sankhya_Intelligence_PCB_Gerbers v1 41526.zip and extract it. Inside you will find three files: Gerbers.zip, BOM.csv, and PickAndPlace.csv.

02

Upload Gerbers.zip to JLCPCB

Go to jlcpcb.com → Quote Now → upload Gerbers.zip (the inner zip, not the outer one you just extracted). JLCPCB auto-detects board dimensions. Select: 2 layers, FR4, 1.6 mm thickness, HASL surface finish, your preferred quantity.

5 boards typically cost under $10 + shipping. Minimum order is 5 pieces.

03

Enable PCB Assembly (PCBA) — optional but recommended

Toggle PCB Assembly on the same order page. Upload BOM.csv and PickAndPlace.csv when prompted. JLCPCB will source and solder all SMD components from LCSC. Through-hole parts (terminal blocks, barrel jack, USB-A port) are not assembled by PCBA — you solder those yourself.

The Waveshare ESP32-S3-Nano is not included in the PCBA — it sockets into the board after delivery.

04

Socket and solder the ESP32-S3-Nano

The Nano plugs into the 2×female 2.54 mm headers on the back of the board. Solder it in — friction fit alone will fail under vibration and thermal cycling in a field enclosure. Use the Waveshare ESP32-S3-Nano specifically — pin spacing and GPIO numbering differ from other Nano-form-factor modules.

05

Flash firmware and register your node

Visit flash.sankhyafarms.com, connect via USB-C, select your sensor configuration, and flash. Then register the node with your Sankhya Intelligence account to begin continuous per-tree soil data collection and AI analysis.

Frequently asked questions

FAQ

No. One 4G LTE dongle plugs into the USB-A port on a single designated node and creates a WiFi hotspot. All other nodes connect to that hotspot as WiFi clients. Use a TP-Link TL-WR845N or similar repeater to extend coverage across all irrigation zones. One SIM card and one data plan serves the whole orchard.
The USB-A port carries 5V and GND only — there are no data lines connected to the ESP32. It exists solely to power a 4G LTE WiFi dongle. Storage devices, keyboards, or any USB data device will not function. If you are within range of existing WiFi infrastructure, no dongle is needed at all — the ESP32 connects directly to any WiFi network.
LiFePO₄ is significantly safer for unattended outdoor deployment — no thermal runaway risk at high temperatures inside a sealed enclosure in direct sun. It also has a flat discharge curve, keeping voltage stable until nearly empty, which makes battery state-of-charge estimation more reliable. The internal BMS handles charge ceiling so no separate solar charge controller is needed.
The LiFePO₄ battery's internal BMS acts as the charge ceiling — it disconnects charge input when full. An SS34 Schottky diode between the solar panel and battery prevents backfeed current at night. This is fully sufficient for 20W panel wattage. A dedicated MPPT controller adds cost and complexity for marginal efficiency gain at this scale.
If your sensor speaks Modbus RTU over RS-485, it will physically work with this board. The Sankhya Intelligence platform requires a verified register map to parse sensor data correctly. If your sensor is not in our sensor library, send us the datasheet — if it speaks Modbus RTU we can add support within days.
That is the EasyEDA account username of Engr. Kashif, the PCB designer who produced this board under a work-for-hire commission. The Gerber files, BOM, and PickAndPlace CSV contain no personal identifying information and are safe to use directly with JLCPCB.
Because the hardware is genuinely not the hard part. The BOM is commodity components from LCSC that anyone can order. What takes years to build is the per-tree longitudinal dataset, the uptake index model, and the agronomic AI layer — all of which run entirely server-side. Publishing the hardware removes the adoption barrier, enables self-installation, and turns transparency into a competitive signal. Fasal does not do this. Netafim certainly does not.