Advances in Plant Electrical Signals for Plant Health Monitoring and Smart Agriculture

Plant electrical signals are fast electrophysiological responses to environmental stimuli, physiological regulation and stress. Because they often change before visible symptoms appear, they are attractive indicators for early plant-health monitoring and for sensing in precision agriculture. Research in this area has moved from recording single action potentials to analyzing the plant electrome, and now draws on flexible wearable electrodes, implantable and electrochemical sensors, machine learning, multi-source data fusion and edge-cloud computing. This review follows the technical chain from mechanism to application. We first summarize the physiological basis of the main signal types and how they relate to biotic and abiotic stress. We then compare acquisition technologies, including conventional electrodes, flexible wearable devices, microneedles and electrochemical sensors for biochemical markers. Next, we discuss preprocessing, multi-scale feature extraction, and recognition with conventional machine learning, deep learning and multi-source fusion. Finally, we examine applications in environmental sensing and stress identification and outline the remaining obstacles to field use. Most published results still come from laboratory or greenhouse studies with small datasets and non-standard protocols. Progress toward field deployment will depend on standardized acquisition and reporting, long-term sensor stability, cross-species and cross-site validation, open datasets, interpretable models and low-power edge inference. With these elements in place, plant electrical signals could serve as one component of decision-support systems for early warning and crop management.