Precision irrigation with AI-driven optimization of plant electrophysiology

Abstract

As global water scarcity intensifies and agricultural demands rise, there is a critical need for efficient irrigation management systems. Traditional autonomous irrigation solutions often depend on soil moisture and environmental sensors that indirectly reflect plant water status, leading to suboptimal irrigation practices. In this study, we introduce an innovative AI-powered autonomous irrigation system that leverages plant electrophysiological (EP) signals to directly monitor real-time plant water status for the first time. Our system integrates EP sensors, real-time signal acquisition and processing, and a convolutional neural network (CNN)-based predictive model to optimize irrigation conditions. Results indicate that EP signals can effectively differentiate between various irrigation levels with a temporal resolution of seconds, significantly enhancing water-use efficiency through real-time feedback. By optimizing water consumption using the AI algorithm, our approach can achieve at least a 10 % reduction in water use while maintaining optimal water conditions for crops. This method represents a promising advancement for precision agriculture and sustainable water management.