It’s using machine learning and computer vision to learn from farm data (images, sensor readings, weather history) and then detect issues, predict outcomes, or recommend actions. Think: smarter decisions, not automatic farming.

Yes—especially advisory tools like weather guidance, disease-risk alerts, and simple diagnostics. The key is affordability, local relevance, and workflows that still work with limited connectivity.

It can, by enabling targeted application and early detection. But the savings show up only when the tool is accurate in your conditions and the recommendations are actually followed in the field.

Pick one pain point: irrigation scheduling, crop monitoring, or basic pest/disease detection. Don’t try to “AI everything” at once—start small, measure, then expand.