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Title: Plant pest detection using an artificial nose system: A review

item CUI, SHAOQING - The Ohio State University
item LING, PETER - The Ohio State University
item Zhu, Heping
item KEENER, HAROLD - The Ohio State University

Submitted to: Sensors
Publication Type: Review Article
Publication Acceptance Date: 5/18/2018
Publication Date: 6/8/2018
Citation: Cui, S., Ling, P., Zhu, H., Keener, H. 2018. Plant pest detection using an artificial nose system: A review. Sensors. 18(2):378-396.

Interpretive Summary: Early detection of infested and infected plants prior to the onset of visual symptoms can assist growers to choose appropriate pest manage strategies to reduce economic and production losses. Plants release a large amount of volatile organic compounds (VOCs) for combatting disease infections, insect attacks, and mechanical damage. Numerical investigations have been reported on using electronic noses (E-nose) to detect plant VOCs. In this paper, the novel E-nose technology was comprehensively reviewed for diagnosing infested and infected plants, including sensor array, sampling set design, pattern recognition, and critical limitation. Potential advantages of E-nose are the non-contact sense, speed, ease of operation, and range of VOC selections. However, challenges remain in respect to the sensor selectivity, interference from the surrounding atmosphere, and detection difficulty in open fields. Future improvements on the E-nose technology were discussed to achieve satisfactory sensor performance for accurate plant health diagnosis.

Technical Abstract: This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provides functional information about the plant’s growth, defense, and health status, allows for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms. Compared with traditional gas chromatography-mass spectrometry (GC-MS) techniques, electronic noses are noninvasive and can be rapid, cost-effective for a number of applications. However, using electronic noses for plant pest diagnosis is still in its early stages, and there are challenges regarding sensor performance, sampling and detection in open areas, and scaling up measurements. This review paper introduces each element of electronic nose systems, especially commonly used sensors and pattern recognition methods, including their advantages and limitations. It includes a comprehensive comparison and summary of applications of electronic nose systems for different plant pest diagnoses, possible challenges in plant pest diagnosis, and potential improvements.