Machine learning for early detection of soil pests using volatile organic compound profiling
A noninvasive detection platform that uses gas chromatography–mass spectrometry combined with machine learning to identify volatile profiles of pest and beneficial organisms in soil. Enables early pest detection before visible damage, supporting turf management, agriculture, and integrated pest management decisions.
Overview
This solution offers a noninvasive method for early detection and quantification of soil-dwelling pests by analyzing volatile organic compound (VOC) profiles above soil. By combining gas chromatography–mass spectrometry (GC-MS) with supervised and unsupervised machine learning algorithms, the approach can distinguish among soils containing grubs, entomopathogenic nematodes, entomopathogenic fungi, and pest-free controls. Detection occurs before visible signs of turf or crop damage, enabling proactive pest management decisions that reduce pesticide use, protect beneficial organisms, and lower production losses.
Technical specifications
- Sample collection: Headspace volatile collections captured above turf or soil without disturbing the growing medium
- Analytical platform: GC-MS profiling of soil headspace volatiles to generate chemical fingerprints
- Machine learning pipeline: Supervised and unsupervised algorithms trained to classify and differentiate pest, beneficial organism, and control samples
- Noninvasive workflow: Sampling performed without physical disruption of turf, preserving soil structure and root systems
- Extensible scope: Initial validation across multiple organism types, with planned expansion to additional soil-borne pests
- Field-ready design: Approach developed in controlled microcosms and intended for deployment in field experiments and commercial field mapping assays
Technology readiness level
The technology has been validated in controlled microcosm experiments, where machine learning models successfully identified the presence of pest and beneficial organisms from soil volatile profiles prior to visible damage. Current readiness corresponds to early-stage proof of concept validated under controlled conditions. Next steps include field-based validation in controlled experiments and commercial field mapping assays, expansion to additional soil-borne pest species, and assessment of broader extensibility across cropping systems.
About Cornell University
Cornell University is a comprehensive private, land-grant research university with campuses in Ithaca and New York City, combining significant scale with cross-disciplinary breadth. Industry connects through open-access user facilities and prototyping labs, pilot-scale testbeds, and a research and technology park that provide pathways from discovery to demonstration. A statewide extension network and integration with a major hospital system enable real-world deployment, while a graduate campus embedded in New York City’s tech corridor provides direct access to startups, venture investors, and corporate R&D teams. Research is supported by competitive federal funding from agencies such as the National Science Foundation, National Institutes of Health, the Department of Energy, and the U.S. Department of Agriculture. A dedicated technology transfer office streamlines IP management, licensing, startup formation, and corporate partnerships across campuses.
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