Autonomous, AI-driven crop management based on electrophysiological plant signals

The project focuses on the practical development and validation of innovative biosensors and AI software that analyze electrophysiological signals from plants for the early detection of stressors. By enabling direct, real-time feedback from the plant, growers can implement more targeted, efficient, and sustainable cultivation interventions. This contributes to higher yields, reduced resource use, and a future-proof, data-driven agriculture and horticulture sector.