Real-time ion concentration pattern analysis in plants based on microneedle-type sensing and time-series prediction

Abstract

In situ detection of plant ion signals faces technical limitations in terms of real-time capability, minimal invasiveness, and data analysis. Therefore, the development of sensors for in vivo plant detection and construction of time-series prediction models to analyze the dynamic patterns of ion concentrations in plants are imperative. This study presents a microneedle electrode system for potassium ion (K⁺) sensing, which is applied to real-time in situ detection in lettuce. The microneedle ion-selective electrodes (ISEs) fabricated herein exhibited a rapid potentiometric response (within < 15 s), with concentration responses adhering to the Nernst equation. During in vivo plant detection, the system captured instantaneous ion-signal changes upon exogenous application without influencing subsequent plant growth. This study demonstrates the pioneering application of time-series prediction (nonlinear autoregressive neural network model) to analyze in vivo K⁺ signals in lettuce, accurately forecasting ion concentration dynamics over time and identifying the transition pattern from signal fluctuation to stabilization. The integration of microneedle ISE-based in situ plant monitoring with time-series prediction represents a crucial and reliable approach to agricultural sensor innovation, providing a novel paradigm for precision agriculture and plant stress response research.

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