Journal article

Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling

Sigfredo Fuentes, Eden Tongson, Ranjith R Unnithan, Claudia Gonzalez Viejo

SENSORS | MDPI | Published : 2021

Abstract

Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based ..

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Grants

Awarded by Australian Grain Pest Innovation Program, an investment from the Grains Research and Development Corporation (GRDC), Australia


Awarded by Bayer Grants4Ag sustainability-focused program


Funding Acknowledgements

This research was supported by seed funding from (i) the Australian Grain Pest Innovation Program, an investment from the Grains Research and Development Corporation (GRDC), Australia (ID: 2062311) and (ii) Bayer Grants4Ag sustainability-focused program (ID: 106027).