Decoding in-situ plant stress physiology via bio-hydrogel microneedles.
TL;DR
Using an intelligent diagnostic model based on stress-evoked H2O2 dynamics, CRH-BMIS achieves competitive cross-plant classification of abiotic/biotic stress and disease-stage classification and superior performance stems from the deep synergy between minimally invasive microneedles and machine learning algorithms.
Abstract
Electrochemical detection of hydrogen peroxide (H2O2) dynamics enables early monitoring of plant stress physiology, which is crucial for crop health management and yield optimization. However, invasive sensors damage plant tissues, whereas existing non-invasive approaches suffer from cuticular barriers and insufficient exploitation of dynamic biochemical signals, leading to high interfacial impedance, delayed responses, and low accuracy. Here, we introduce an integrated sensing and machine-learning framework (CRH-BMIS) that combines a highly specific Cs/rGO/HRP biohydrogel microneedle with algorithmic analytics. This system overcomes traditional barriers to in situ, tissue-level monitoring of abiotic and Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) stresses. By significantly lowering interfacial impedance, the microneedle exhibits a stable log-linear response over an ultra-broad range (0.01-8000 μM) with exceptional sensitivity (up to 12.46 μA cm-2 decade-1, LOD 0.00109 μM) and robust anti-interference. Furthermore, utilizing an intelligent diagnostic model based on stress-evoked H2O2 dynamics, CRH-BMIS achieves competitive cross-plant classification of abiotic/biotic stress (96.97% accuracy) and disease-stage classification (91.47% accuracy). This superior performance stems from the deep synergy between minimally invasive microneedles and machine learning algorithms. CRH-BMIS not only delivers accurate, generalizable in situ stress diagnosis but also enables scalable early-warning decision support.