Machine learning-enabled implantable plant biomarker sensor for early detection and classification of acid and salt stress.

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 H 2 O 2 , K + , and pH. We confirm the robust sensing capabilities and favorable biocompatibility of MLIPBS through cross-species validation in lettuce, tomato, and Aloe vera. Additionally, leveraging the LightGBM architecture, we demonstrate that MLIPBS successfully classifies combined stress conditions and varying intensity levels of acid and salt stress, achieving an average accuracy of 90.5%. We further show that the system identifies stress types and intensities within 8 hours of onset, providing an early-warning window at least 48 hours before symptom manifestation. Our study provides reliable wearable tools for stress-resistant crop screening and precision management in smart agriculture.

Code and data availability

The supplied blocks contain no public phenotype/trait datasets, sensor data, plant images, author analysis code, or trained model deposits. The paper describes an implantable electrochemical sensor with LightGBM classification, but no data or code availability statement with a public repository URL appears in the text.

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