Yibin Ying

Abstract In vivo analysis of plant stress responses remains a major challenge in precision agriculture, limiting dynamic optimization of crop growth under variable environmental conditions. Fluorescence imaging enables nondestructive tracking of stress biomarkers, but its accuracy is compromised by the low abundance of endogenous signaling molecules, tissue autofluorescence interference, and limited signal penetration depth.

Abstract The human fingertip skin, with its interlocked epidermal-dermal architecture and dense tactile receptors, enables unique perception and inspires the design of innovative electronic skin (e-skin). However, most e-skin materials fail to replicate the hierarchical integration of composition, structure, and function found in natural skin.

Abstract Abiotic stresses, particularly acid and salt stress, severely limit plant productivity. Conventional detection is often hindered by physiological lags and phenotypic latency. Here, we develop a machine learning-enabled implantable plant biomarker sensor (MLIPBS) for early stress diagnosis. Featuring a foldable design, MLIPBS enables conformal integration into plant tissues for continuous monitoring of H2O2, K+, and pH.