Explore the use of advanced technologies such as GPS, drones, sensors, and data analytics in agriculture to enhance productivity, reduce resource wastage, and make data-driven decisions.
Precision Agriculture Technologies – Study Notes
| Component | Function | Typical Technologies |
|---|---|---|
| Data Acquisition | Collect real‑time information on soil, plants, weather, and equipment. | • GPS/RTK receivers • Satellite & drone imagery • Ground‑based sensors (soil moisture, EC, temperature) • Weather stations |
| Data Management | Store, clean, and integrate heterogeneous datasets. | • Cloud platforms (AWS, Azure, Google Cloud) • Farm Management Information Systems (FMIS) • Data lakes & relational databases |
| Analytics & Modeling | Transform raw data into actionable insights. | • GIS mapping • Machine‑learning models (yield prediction, disease detection) • Decision support tools (prescription maps) |
| Automation & Actuation | Apply inputs (seed, fertilizer, pesticide, water) precisely where needed. | • Variable‑rate technology (VRT) • Autonomous tractors & sprayers • Robotic weeders & harvesters • IoT‑controlled irrigation valves |
| Feedback Loop | Monitor outcomes, refine models, and close the cycle. | • Yield monitors • Post‑harvest sensor data • Continuous learning algorithms |
| Platform | Spatial Resolution | Typical Use |
|---|---|---|
| Satellite (e.g., Sentinel‑2, PlanetScope) | 3–10 m | Large‑area vegetation indices, drought monitoring |
| Manned Aircraft | <1 m | Detailed canopy health, biomass estimation |
| Unmanned Aerial Vehicles (UAVs) | 2–10 cm | Spot scouting, disease detection, weed mapping |
| Ground‑Based Sensors (LiDAR, multispectral cameras) | <5 cm | Row‑level plant height, canopy structure |