Precision Agriculture and IoT Platform Development

Soil sensor data integration across multiple sensor hardware manufacturers -- Sentek, Decagon, Delta-T, Irrometer, and proprietary sensor hardware -- into a single field data platform regardless of the vendor's native data format or API structure. Soil moisture, temperature, and electrical conductivity data from multi-depth probe installations, 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, with notifications sent 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 when a sensor stops transmitting data, distinguishing between a sensor fault and a connectivity issue. Historical sensor data stored against the field record for multi-season analysis and for 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 using the FAO Penman-Monteith method, used 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 to supplement local station data for forward-planning decisions -- the next seven days of temperature and rainfall probability displayed alongside the current soil moisture status for each irrigated block. Growing degree day accumulation tracking for 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 showing 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, with NDVI map generation and export in formats usable by precision application equipment. Anomaly detection within fields -- areas of crop stress, waterlogging, disease pressure, or pest damage visible in the imagery before they are apparent from ground-level inspection. Imagery archived against the field record for the season so historical comparisons are 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 to generate continuous soil property maps from point sample data. Map export in ISO-XML format for compatibility with the field computers on John Deere, Case IH, AGCO, and Fendt machinery, and in shape file format for precision application companies using their own controllers. As-applied data import from machinery telematics after field operations to verify that the prescribed rates were actually applied and to record any deviations from the prescription for the agronomic record. Prescription tracking over multiple seasons to build 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, calculating 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 recorded against the field record for each irrigation run. Automated irrigation trigger for operations with controllable irrigation systems, with the scheduling platform sending 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 running total visible to farm management for compliance with abstraction licence conditions. Pump station monitoring integration where pump telemetry is available, with fault alerts and flow verification against the irrigation schedule.

Unified operational dashboard displaying all precision agriculture data for the full farm -- soil moisture by field, satellite imagery, current weather and forecast, active alerts, and upcoming irrigation events -- in a single view accessible on desktop and mobile. Field performance summary by season with yield data, input costs, and sensor data overlaid 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 -- CSV, shape file, and ISO-XML exports of 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 to demonstrate the yield and input cost trends that justify the ongoing sensor and imagery investment.