Smart Agriculture IoT Planner
Animation
Beginner first
Precision agriculture
Smart Agriculture IoT Planner
Build a first-pass IoT deployment plan for a farm. Watch sensors appear across
the field, gateway coverage expand, and irrigation decisions update as the
monitoring strategy changes.
0 nodes
Sensor plan
0 m3/year
Estimated water saved
0 years
Simple payback
Try Load the first Crop preset, begin with 1 field zone, and press Step through sensing and irrigation planning.
Observe With each Step, Water demand, soil status, valve schedule, and projected savings change when crop, area, moisture, or weather moves.
Explain For 1 field zone, root-zone deficit times area becomes irrigation volume, then flow capacity schedules that demand rather than treating moisture percentage as volume.
Technical boundaries Evapotranspiration is simplified to fixture values; soil layers, runoff, infiltration dynamics, forecast error, pump curves, crop stress, and sensor drift are omitted.
Colour key
Start with the field
Area and zones decide how many places need sensing, actuation, and maintenance access.
Measure what changes
Soil moisture, temperature, humidity, pH, and water quality are useful when they trigger decisions.
Connect the hard places
Range, terrain, buildings, and power matter more than the advertised radio range on a datasheet.
Prove value safely
Use a pilot first, then automate irrigation only after thresholds and fail-safes are validated.
1. Scout Map zones and local variability.
2. Sense Place sensor nodes where data changes.
3. Connect Choose gateways and radio coverage.
4. Decide Turn readings into irrigation actions.
5. Irrigate Apply water only where needed.
6. Learn Compare outcomes and scale carefully.
Animated Farm Deployment Plan
Start by mapping zones, field size, and variability before buying sensors.
Planning stage
Scout
Smart agriculture deployment animation
Field zones, sensor nodes, gateway coverage, irrigation water, and planning feedback change as controls are adjusted.
Gateway
1 gateway
Decision engine
Moisture + weather + thresholds
Readiness 0%
Pilot plan
Start with 0 nodes
Annual value
$0/year net
field map
Deployment scale
0 nodes
Sensor count is based on area, density, and variability.
Connectivity fit
LoRaWAN
Gateway count is estimated from effective outdoor coverage, not ideal maximum range.
Recommendation
Run a pilot
Pilot first, then expand once data quality and thresholds are trusted.
Experiment Controls
Choose a farm type, then change one assumption at a time. The field map, sensor count, coverage, and value estimate update together.
Play
Step
Reset
Hard case
Crop farm
Vineyard
Greenhouse
Livestock
Aquaculture
Automation level
Monitoring only
Advisory alerts
Automated zones
Closed-loop control
Connectivity
LoRaWAN gateways
Wi-Fi / farm LAN
Cellular nodes
Low-power mesh
Power plan
Battery
Solar + battery
Mains / greenhouse power
Reference Material
These cards turn the planner into a repeatable checklist for agriculture IoT chapters and field exercises.
Sensor Selection Quick Reference
Soil moisture Most useful for irrigation scheduling. Place at crop root depth and calibrate for soil type where possible.
Temperature Tracks frost, heat stress, pest pressure, and greenhouse control. Shield sensors from direct sun.
Humidity Useful for disease-risk models and greenhouse control. Combine with temperature and leaf wetness when possible.
Weather station Rain, wind, solar radiation, and evapotranspiration estimates improve irrigation decisions beyond soil readings alone.
Flow meter Confirms whether irrigation actually happened. It catches blocked valves, leaks, and pump failures.
pH, EC, NPK Useful for nutrient management, hydroponics, vineyards, and aquaculture. Budget for calibration and maintenance.
Deployment Pattern Guide
Grid pattern Good for broadacre crops with uniform irrigation zones. Increase density where soil changes quickly.
Row pattern Good for vineyards and orchards. Follow rows and monitor top, middle, and bottom microclimates.
Greenhouse zones Use tighter spacing because small climate changes can affect yield and disease pressure quickly.
Livestock gateways Use fixed gateways plus mobile tags or water-point sensors. Coverage gaps matter more than exact field grid density.
Aquaculture depth Measure water quality at relevant depths and locations. Surface readings alone can hide oxygen or pH gradients.
Pilot first Start with a representative block, verify connectivity and data quality, then scale after thresholds are trusted.
Technical Accuracy Notes
Water conversion One millimeter of water over one hectare is 10 cubic meters. This model uses that conversion for irrigation savings.
Sensor density Density is a planning assumption, not a universal rule. Soil, slope, crop, and management zones decide real placement.
Radio coverage Effective coverage is smaller than ideal range because terrain, vegetation, antenna height, and gateway placement matter.
ROI limits The payback estimate excludes financing, tax, grants, downtime, data science labor, and crop-price uncertainty.
Automation safety Closed-loop irrigation needs manual override, valve feedback, pump protection, weather lockout, and sensible thresholds.
Calibration Water, pH, EC, and nutrient sensors drift. Maintenance budget and calibration schedule are part of the deployment.
Related Practice
Use these after the agriculture plan to test business value, connectivity, and deployment readiness.