What agri-businesses in India need
A progressive farmer or a polyhouse operator wants to know soil moisture, temperature and humidity by zone, and to switch pumps and valves on a schedule or from a phone. A farmer producer organisation needs member records, procurement with grading and weighing, payments to farmers, input sales with GST and a stock position for the warehouse. A cold storage or warehouse operator needs temperature logs, alerts and inventory by lot.
Agri-input dealers, traders and processors sit in between, with credit ledgers, seasonal cash cycles and a lot of WhatsApp. Almost everyone wants advisory: when to irrigate, when to spray, what the market rate is, in Marathi or Hindi, as a voice note or a picture rather than a paragraph.
The common thread is that the data lives in the field, the decisions are made on a phone, and the person making them may have limited literacy, an old Android and no signal.
The hard parts
- Power and connectivity in the field. Sensor nodes need to run on solar and batteries for months. Data travels over LoRaWAN to a gateway with GSM backhaul, because there is often no Wi-Fi or reliable 4G at the plot.
- Hardware in dust, heat and monsoon. Cheap sensors drift and die. Enclosures, cable glands, lightning protection and calibration schedules matter as much as the code.
- Data quality. A soil moisture reading means nothing without calibration for that soil. Analytics built on uncalibrated sensors produces confident nonsense. We budget for ground-truthing with the farmer.
- Agronomy is local. Crop models from elsewhere rarely fit a Vidarbha soybean field. Useful prediction needs local weather, local yields and a season or two of data before it is trusted.
- Users and devices. Interfaces need Marathi and Hindi, icons over text, voice where possible, and must work on low-end phones with intermittent connectivity. WhatsApp is the notification channel that actually gets read.
- Seasonality and money. FPO software has to handle procurement in a burst, farmer payments in batches, GST on inputs and exemptions on produce as your accountant defines them, and audit trails for board and lender reporting.
What we build
- Field sensor networks for soil moisture, temperature, humidity, water level and tank monitoring, on LoRaWAN, GSM or Wi-Fi.
- Automated irrigation: pump and valve control on schedules, thresholds or manual override from a phone, with fail-safes when the link drops.
- Weather integration and forecasts combined with sensor data for irrigation and spray advisories.
- Farm dashboards and mobile apps showing zones, alerts and history, in Marathi, Hindi and English.
- Crop analytics and machine-learning models trained on your own field data once enough has been collected.
- FPO and agri-business management: members, procurement, grading, weighbridge integration, farmer payments through UPI or bank, input sales with GST, stock and reporting.
- Cold storage and warehouse monitoring with temperature logs, alerts and lot-wise inventory.
- Traceability from plot to lot to buyer, with QR codes where a buyer requires it.
A typical stack
Sensor nodes are built on ESP32 or Arduino-class microcontrollers with LoRaWAN radios, talking to a gateway that forwards over GSM. Data arrives through MQTT into a Python service and lands in PostgreSQL with TimescaleDB for time series. Dashboards are React or Next.js; farmer apps are React Native or Flutter with offline caching. Models are Python, retrained as seasons pass. Alerts go out through the WhatsApp Business API and SMS.
| Component | Our default | Alternative and when |
|---|---|---|
| Field radio | LoRaWAN, several kilometres per gateway | GSM per node when nodes are few and far apart |
| Time-series data | PostgreSQL with TimescaleDB | InfluxDB when sensor count is very high |
| Farmer interface | WhatsApp alerts plus a light Android app | IVR calls where phones are basic |
As a rough guide, a pilot with 10–20 sensor nodes, a gateway, irrigation control and a dashboard lands around ₹3–8 lakh including hardware, and FPO management software around ₹3–10 lakh. Scaling and agronomy modelling are scoped after the pilot.
Related work
Farmers needed crop monitoring, automated irrigation and weather prediction. We built an IoT sensor network feeding machine-learning crop analytics and a web dashboard, on Python, Arduino, LoRaWAN, machine learning and React. It is summarised in our work section, and we can describe the hardware choices and what we would change on the next deployment.
Talk to us
If you run a farm, an FPO, a cold storage or an agri-input business and want sensors, automation or management software that survives a Vidarbha summer, tell us about the site. We scope this work under IoT and hardware integration, AI and automation and custom software development.
