Think of a farm where every plant has a tiny ‘weather station’ and health monitor, and all of those monitors report back in real time to a digital farm manager. That manager tells you exactly where to water, fertilize, or treat for pests so you don’t waste inputs and you get more yield from the same land.
Traditional farming treats whole fields the same, wasting water, fertilizer, and labor while missing localized problems like dry patches or pest outbreaks. IoT-based precision agriculture uses connected sensors and devices to continuously measure soil, crop, and environmental conditions so farmers can act precisely—reducing input costs, increasing yield quality, and improving resource efficiency.
Integration of sensor networks with agronomic know‑how, historical farm data, and localized models; the difficulty of swapping out once deployed across fields creates a sticky, long‑term relationship with farmers and agribusinesses.
Unknown
Time-Series DB
High (Custom Models/Infra)
Network coverage and bandwidth in rural areas, power constraints for distributed sensors, and managing/cleaning large volumes of noisy sensor time-series data.
Early Majority
Focus on highly precise, sensor-driven decision-making for agriculture fields—going beyond generic IoT by tailoring devices, analytics, and control loops to agronomic parameters like soil moisture, nutrient levels, and microclimate variations.
109 use cases in this application