Precision agriculture, in the US Department of Agriculture’s framing, is a general term for farming based on observing, measuring, and responding to variability within a field. A single field is rarely uniform: soil type, moisture, nutrients, and pest pressure differ from one corner to the next. Instead of treating the whole field identically, precision agriculture tailors inputs - seed, fertilizer, water, herbicide - to what each patch of ground actually needs.
The foundation was satellite positioning. GPS and GNSS let machinery know its location to within a centimeter, enabling autosteer (GPS-guided tractors that drive straighter than any human), variable rate technology (machinery that changes the application rate of fertilizer or chemicals as it crosses the field), and yield monitors that build spatial maps of how much grain came off each part of a field. Layered on top of this geospatial backbone are sensors, drones, and increasingly AI: computer-vision systems that distinguish a weed from a crop in real time, and models that fuse imagery and sensor data into recommendations.
The USDA notes that adoption skews toward larger operations - autosteer is common on big row-crop farms - while the small farms that make up the bulk of US operations have adopted more slowly. The promised benefits are both economic and environmental: less wasted fuel, seed, and chemical, lower costs, and reduced nutrient runoff. AI-driven systems such as John Deere’s See and Spray, which sprays herbicide only on detected weeds, represent the leading edge, where machine perception decides input application plant by plant.
Why business readers should care: precision agriculture is where decades of GPS, sensors, and now computer vision compound into measurable input savings. It is a concrete example of AI’s value showing up not as a flashy product but as a few percent shaved off the cost of every acre, multiplied across millions of them.