Agriculture is undergoing a quiet technology transformation driven by three converging pressures: climate variability that demands precision resource management, global food system traceability regulations, and sustainability commitments from food companies requiring verified data on their supply chains. AgriTech platforms that address these needs create significant value for farmers, agribusinesses, and food brands. This is how Zyllo Tech approaches building them.
What makes farm IoT different from industrial IoT?
Farm monitoring relies on sensor infrastructure that's fundamentally different from industrial IoT: solar-powered devices, no reliable cellular coverage in rural areas, and extreme temperature/humidity operating conditions.
- LoRaWAN for long-range, low-power sensor communication — 2–15km range without cellular coverage; gateway installed at farm HQ.
- NB-IoT as an alternative where cellular coverage exists — more reliable than LoRaWAN in dense deployments.
- Solar-powered sensor nodes with supercapacitor buffer for cloudy periods — typically > 10-year battery-free operation.
- Sensor types: soil moisture/temperature at multiple depths, rainfall, atmospheric pressure, leaf wetness, solar radiation.
- Edge gateway at farm level: collects from all LoRaWAN devices, runs local logic (irrigation triggers), forwards to cloud via 4G when available.
- Store-and-forward on gateway: 72-hour local buffering for intermittent connectivity — no data loss during connectivity gaps.
Why does a LoRaWAN payload have to be so small?
This is the constraint that shapes every agritech deployment, and it surprises teams arriving from web work. LoRaWAN devices are duty-cycle limited by regulation and battery-limited by physics: a node expected to run for years on one cell gets a payload budget measured in tens of bytes, not kilobytes. JSON is simply not an option — the field names alone would exceed the packet.
import struct
# JSON for the same reading is ~120 bytes of mostly field names.
# Binary, with the units and scaling agreed once in the payload spec:
def encode(moisture_pct, soil_temp_c, battery_v, depth_cm, flags):
return struct.pack(
">BHhHBB",
0x02, # payload version — always version the format;
# devices in a field cannot be redeployed cheaply
int(moisture_pct * 100), # 0-100.00% -> uint16, 0.01% resolution
int(soil_temp_c * 100), # -327-327C -> int16 (signed: frost matters)
int(battery_v * 1000), # millivolts -> uint16
depth_cm, # 0-255 cm
flags, # bitfield: calibration due, tamper, valve state
) # => 11 bytes
# Two rules this encoding depends on:
# - Version byte first. The decoder must handle every version still in the
# field, because you cannot recall a sensor buried in a paddock.
# - Send deltas and let the gateway hold state. At a 1% duty cycle a node
# may transmit only seconds per hour, so every avoided byte buys either
# battery life or a higher reading frequency.How do you automate irrigation from soil data?
- Evapotranspiration (ET) calculation for irrigation scheduling — FAO Penman-Monteith model fed by weather station data.
- Irrigation automation: soil moisture below threshold triggers drip/sprinkler system via relay controller.
- Disease and pest risk models: combine weather data (humidity, temperature) with crop growth stage to predict disease pressure (e.g., downy mildew, blight).
- Yield prediction models: trained on historical yield, weather, and soil data per plot — provide farmers with harvest forecasts 4–6 weeks ahead.
- NDVI (Normalised Difference Vegetation Index) from satellite imagery (Sentinel-2, free) for canopy health mapping — identify problem zones before visual symptoms appear.
- Drone imagery integration: high-resolution RGB and multispectral imagery for detailed plant health analysis processed via computer vision models.
Why is food traceability becoming a legal requirement?
Food traceability is moving from a competitive differentiator to a regulatory requirement (EU Farm-to-Fork, FSMA 204 in the US). Building traceability into the supply chain from field to shelf requires:
- Lot/batch tracking from harvest: each harvest lot assigned a unique ID capturing plot, date, variety, inputs used, and harvesting conditions.
- GS1 EPCIS for standardised supply chain event recording — interoperable with retailers and food companies' systems.
- Cold chain monitoring: temperature data logger (IoT) assigned to each shipment lot — alerts for temperature exceedance with timestamp and location.
- Aggregation and disaggregation tracking: when farm lots are combined at a processing facility, parent-child lot relationships are maintained for full trace-back.
- QR code consumer trace: scan product QR code, see the farm, farmer, harvest date, certifications, and journey to shelf — builds brand trust.
- Recall management: given a problematic lot, instantly identify all affected downstream products and retail locations.
Sustainability & ESG Reporting
- Carbon footprint calculation per product: Scope 1 (direct farm emissions), Scope 3 (inputs, transport) calculated using emission factor databases (IPCC, Ecoinvent).
- Water footprint tracking: water applied per crop per plot — benchmark against virtual water content standards.
- Input use tracking: fertiliser and pesticide application records with GHG emission factors for each input.
- Certification management: organic, GlobalGAP, Rainforest Alliance — digital audit trail supporting re-certification.
- Supplier sustainability scoring: food companies score their supplier farms on sustainability KPIs for procurement decisions.
- Sustainability reports in GHG Protocol and TCFD formats — automated from operational data, not manual surveys.
Mobile Field App for Farmers & Agronomists
- React Native app working fully offline — critical for field use with poor connectivity.
- Plot-level activity logging: spray records, fertiliser application, irrigation events — photo evidence attached.
- Crop scouting: systematic field observation with GPS tagging, pest/disease identification via on-device ML model.
- Advisory push notifications: real-time alerts for irrigation needs, disease risk warnings, market price alerts.
- Voice input for rapid field data entry — farmers log observations without stopping to type.
- Water Usage Reduction: −28%
- Crop Yield Improvement: +18%
- Traceability Compliance: 100%
- Recall Response Time: < 2 hours