Detecting Plant VOC Traces Using Indoor Air Quality Sensors

2025-04-03

AI Summary

Indoor air quality (IAQ) monitoring in smart homes and healthy buildings faces challenges in dynamically detecting plant-stress-induced volatile organic compounds (VOCs), particularly terpenes, due to reliance on expensive laboratory-grade instrumentation. Method: This study proposes a novel paradigm for identifying plant-emitted terpenoid VOCs using low-cost, off-the-shelf metal-oxide semiconductor (MOS) IAQ sensors—marking the first application of such sensors to detect and classify biogenic VOCs emitted during live-plant stress responses. We develop a hybrid classification framework integrating physics-informed modeling with supervised machine learning (random forest and SVM), and optimize sensor spatial deployment. Results: We experimentally characterize terpene response profiles across 16 compounds, identify high-sensitivity species, and demonstrate robust real-world classification of stress-induced emissions from basil plants in indoor environments. Our approach achieves significantly higher accuracy than pure physics-based models, establishing a viable, low-cost, deployable pathway for microenvironmental indirect sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Active Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount. Smart tools, especially VOC sensors, are crucial for monitoring indoor air quality, yet interpreting signals from various VOC sources remains challenging. A promising approach involves understanding how indoor plants respond to environmental conditions. Plants produce terpenes, a type of VOC, when exposed to abiotic and biotic stressors - including pathogens, predators, light, and temperature - offering a novel pathway for monitoring indoor air quality. While prior work often relies on specialized laboratory sensors, our research leverages readily available commercial sensors to detect and classify plant emitted VOCs that signify changes in indoor conditions. We quantified the sensitivity of these sensors by measuring 16 terpenes in controlled experiments, then identified and tested the most promising terpenes in realistic environments. We also examined physics based models to map VOC responses but found them lacking for real world complexity. Consequently, we trained machine learning models to classify terpenes using commercial sensors and identified optimal sensor placement. To validate this approach, we analyzed emissions from a living basil plant, successfully detecting terpene output. Our findings establish a foundation for overcoming challenges in plant VOC detection, paving the way for advanced plant based sensors to enhance indoor environmental quality in future smart buildings.

Problem

• Detecting plant-emitted VOCs using commercial sensors
• Classifying terpenes to monitor indoor air quality
• Optimizing sensor placement for VOC detection accuracy

Innovation

• Uses commercial sensors for VOC detection
• Trains machine learning to classify terpenes
• Tests sensor placement in real environments