Electronic nose for agricultural grain pest detection, identification, and monitoring: A review

Authors: BADGUJAR, CHETAN - University Of Tennessee; SWAMINATHAN, SAI - University Of Tennessee; Gerken, Alison

Submitted to: Journal of Stored Products Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/28/2025
Publication Date: 10/17/2025
Citation: Badgujar, C.M., Swaminathan, S., Gerken, A.R. 2025. Electronic nose for agricultural grain pest detection, identification, and monitoring: A review. Journal of Stored Products Research. https://doi.org/10.1016/j.jspr.2025.102840.
DOI: https://doi.org/10.1016/j.jspr.2025.102840

Interpretive Summary: Early detection of insect pests and mold in stored grain can save significant losses in nutrition and profit. Current detection methods are based on trap catches or quality testing, which are labor intensive, require extensive expertise to correctly identify insect and mold species, and often underestimate populations. It is also extremely difficult to monitor for pest and mold problems and quality issues in real-time, which allows insect populations and microbes to grow significantly before they are detected and treated. Insects feeding in grain and mold emit odors that could be used to detect infestations in their earlier stages using electronic noses (eNoses), which are sensor systems that mimic the human nose but are far more sensitive. Here, we present a comprehensive review of the use of eNose systems within stored grain environments along with suggestions of how to better implement these sensors. Current data collection has only focused on a few species and commodities, but broadening the types of insects, fungi, and commodities studied will allow for more accurate identification of pest and mold species and better estimations population size. In addition, new machine learning approaches can be used to analyze these large datasets and provide management suggestions in real time. All together, the use of e-nose systems holds potential protecting stored products from economic losses and preventing food safety issues.

Technical Abstract: Biotic pest attacks and infestations, including microbial contamination, are major causes of stored grain losses, leading to significant food and economic losses. Conventional, manual, sampling-based pest recognition methods are labor-intensive, time-consuming, costly, require expertise, and may not even detect hidden infestations. In recent years, the electronic nose (e-nose) approach has emerged as a potential alternative for agricultural grain pest recognition and monitoring. An e-nose mimics human olfactory systems by integrating a sensor array, data acquisition, and analysis for recognizing grain pests by analyzing volatile organic compounds (VOCs) emitted by grain and pests. However, well-documented, curated, and synthesized literature on the use of e-nose technology for grain pest detection is lacking. Therefore, this systematic literature review provides a comprehensive overview of the current state-of-the-art e-nose technology for agricultural grain pest monitoring. The review examines employed sensor technology, targeted pest species type, grain medium, data processing, and pattern recognition techniques. An e-nose is a promising tool that offers a rapid, low-cost, non-destructive solution for detecting, identifying, and monitoring grain pests including microscopic and hidden insects with good accuracy. We identified the factors that influence the e-nose performance, which include pest species, storage duration, temperature, moisture content, and pest density. The major challenges include sensor array optimization or selection, large data processing, poor repeatability, and comparability among measurements. An inexpensive and portable e-nose has the potential to help stakeholders and storage managers take timely and data-driven informed actions or decisions to reduce overall food and economic losses.