Precision Agriculture and IoT Platform Development

Soil sensor data integration across multiple sensor hardware manufacturers into a single field data platform regardless of the vendor's native data format or API structure. Sentek, Decagon, Delta-T, Irrometer, and proprietary sensor hardware are all supported. Soil moisture, temperature, and electrical conductivity data from multi-depth probe installations is visualised at field and zone level with trend charts showing how conditions are changing over time. Alert threshold configuration for each sensor and each depth sends notifications to the farm manager or irrigation operator when moisture falls below the irrigation trigger point for the relevant crop and growth stage. Sensor health monitoring with connectivity alerts distinguishes between a sensor fault and a connectivity issue when a sensor stops transmitting data. Historical sensor data stored against the field record supports multi-season analysis and calibrating irrigation scheduling models with observed field response data.

Local weather station data integration for operations with on-farm weather monitoring. Rainfall, temperature, wind speed and direction, humidity, solar radiation, and leaf wetness recorded at field level rather than relying on the nearest regional weather station. Evapotranspiration calculation from local weather station data uses the FAO Penman-Monteith method as the primary input for irrigation scheduling and for fungicide timing decisions where temperature and leaf wetness models drive spray timing. Regional forecast data integration supplements local station data for forward-planning decisions. The next seven days of temperature and rainfall probability are displayed alongside the current soil moisture status for each irrigated block. Growing degree day accumulation tracking supports pest and disease models that use heat accumulation to predict risk periods and activity emergence dates.

Satellite imagery integration from Planet, Sentinel-2, and other imagery providers with NDVI, NDRE, and other vegetation index calculations applied at field and zone level for crop health monitoring across the growing season. Temporal NDVI trend analysis shows how crop canopy development compares to the previous season and to the expected development curve for the variety and sowing date. Drone imagery processing for operations conducting their own aerial surveys generates NDVI maps and exports in formats usable by precision application equipment. Anomaly detection within fields identifies areas of crop stress, waterlogging, disease pressure, or pest damage visible in the imagery before they're apparent from ground-level inspection. Imagery archived against the field record for the season keeps historical comparisons available for multi-year analysis and for tracing the source of a yield anomaly identified at harvest.

Variable rate application map generation from the combination of soil sampling data, historical yield maps, current season sensor data, and agronomist recommendations. The data sources your agronomist actually uses rather than a single-input model that ignores most of what you know about each field. Soil sampling data integration from laboratory results with spatial interpolation generates continuous soil property maps from point sample data. Map export in ISO-XML format covers compatibility with the field computers on John Deere, Case IH, AGCO, and Fendt machinery, and shape file format covers precision application companies using their own controllers. As-applied data import from machinery telematics after field operations verifies that the prescribed rates were actually applied and records any deviations from the prescription for the agronomic record. Prescription tracking over multiple seasons builds the relationship between variable rate inputs and yield response for progressive refinement of the application algorithm.

Irrigation scheduling based on soil moisture sensor data and evapotranspiration calculations rather than calendar-based schedules that don't respond to actual crop demand and field conditions. Deficit irrigation model for operations managing water allocations calculates crop water demand against available soil moisture to minimise water use while protecting yield. Irrigation event logging with start time, duration, volume applied, and soil moisture response is recorded against the field record for each irrigation run. Automated irrigation trigger for operations with controllable irrigation systems sends start and stop commands directly to the irrigation controller when soil moisture falls to the trigger threshold. Water use reporting against the water allocation for the season with a running total visible to farm management supports compliance with abstraction licence conditions. Pump station monitoring integration where pump telemetry is available provides fault alerts and flow verification against the irrigation schedule.

Unified operational dashboard displaying all precision agriculture data for the full farm in a single view accessible on desktop and mobile. Soil moisture by field, satellite imagery, current weather and forecast, active alerts, and upcoming irrigation events. Field performance summary by season overlays yield data, input costs, and sensor data to identify the fields and zones where the precision investment is generating the highest return. Agronomist portal for agronomists and crop advisors who need access to the farm's sensor and imagery data without accessing the full farm management system. Data export for third-party agronomic tools in CSV, shape file, and ISO-XML formats covers sensor data, imagery analysis, and application maps for use in Trimble Ag Software, SMS Advanced, or other agronomic planning tools. Year-on-year comparison reports for fields under precision management demonstrate the yield and input cost trends that justify the ongoing sensor and imagery investment.