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April 30, 2026Biosensors0 citationsOpen Access

Biosensors for Stress Detection: A Systematic Review from Herbaceous to Woody Plants

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RZRaffaella Margherita ZampieriABAlessandro BizzarriETEleftherios Touloupakis

Key Points

  • The review aims to assess the current state of biosensor technology for stress detection in plants.
  • Systematic assessment using PICO strategy to evaluate relevant research papers.
  • Focus on biosensors for monitoring plant health under various stressors.
  • Analysis of traditional laboratory techniques like GC-MS and HPLC.
  • Biosensors can detect physiological responses to stress before visual symptoms appear.
  • Real-time monitoring approaches improve efficiency compared to traditional methods.
  • Integration of biosensors with AI can enhance environmental monitoring in agriculture.

Abstract

Plants must constantly adapt to biotic and abiotic stressors, which the global climate change crisis has intensified. To monitor plant health and predict their ability to face these challenges, various target molecules, such as hormones, glucose, and reactive oxygen species, are used as proxies for their physiological status. This review provides a systematic assessment of the current state of biosensor technology, an innovative analytical approach designed for in situ, minimally invasive, and real-time monitoring. Using the PICO (Problem, Intervention, Comparison, and Outcome) strategy, relevant research papers were identified. The review highlights how biosensors can detect physiological responses to stress before visual symptoms manifest, offering a significant advantage over traditional, often destructive, laboratory techniques, like gas chromatography–mass spectrometer (GC-MS) or high-performance liquid chromatography (HPLC). These advancements aim to improve precision agriculture and forestry management by providing sustainable methods to assess resilience in changing environments. Finally, the challenges of translating research from model organisms to complex woody species and choosing the correct target are discussed, and future perspectives, including the integration of biosensors with Artificial Intelligence-driven predictive models for large-scale environmental monitoring, are outlined.

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Cite This Study

Zampieri et al. (2026) studied this question.

synapsesocial.com/papers/69f2a4da8c0f03fd67763f47https://doi.org/10.3390/bios16050242
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