Detection of mealybug infestation on the Khasi Mandarin orange plant using electronic nose

Abstract

Mealybugs pose a serious threat to fruit crops leading to premature leaf and fruit drops which severely affects the yield as well as the quality. Primarily pest detection is done with the help of human/animal scouting, which is cumbersome and prone to error. This paper studies the feasibility of using an electronic nose (E-Nose) for detecting mealybug infestation in Khasi Mandarin orange plants. Plants normally release volatile organic compounds (VOCs) which can act as biomarkers for specific stresses affecting the plant. These VOCs can be analyzed to diagnose the plant. VOCs emanating from leaf samples of both infested and healthy plants were analyzed using a custom-made E-Nose system containing an array of commercially available gas sensors. Dimensionality reduction techniques using principle component analysis, and linear discriminant analysis and optimized classification algorithms like support vector machine and random forest were employed to check for the discriminating capability of the E-Nose system. The technique successfully classified samples belonging to infested and healthy categories in both the classifiers with accuracies of 95.66% and 96.70%.

Plant phenotyping relevance

植物葉由来のVOCをE-Noseで測定し、健全・食害状態を分類する方法が研究の中心であり、植物のストレス状態を推定する実質的なセンシング手法である。

Code and data availability

The article describes a custom E-Nose dataset (240×9 sensor responses for 18 plants) and ML analysis, but contains no data or code availability statement, no public repository, and no author-provided URL for datasets, models, or scripts. Only the journal landing page URL appears, which is not a paper-specific asset.

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