AI-driven soil health and crop disease prediction systems combine machine learning, computer vision, and IoT sensors to monitor nutrient content, moisture, pH, and crop stress indicators. Data from drones, satellites, and ground sensors is analyzed to detect diseases and nutrient deficiencies early, enabling targeted interventions. Benefits include increased yield, reduced input costs, and improved sustainability. Leaders like PEAT, Taranis, IBM, and Microsoft are advancing the field, with strong IP and government support for precision agriculture.
