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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (17), p.5948
• Main Authors: Fuentes, Sigfredo, Tongson, Eden, Unnithan, Ranjith R., Gonzalez Viejo, Claudia
• Format: Article
• Language: English
• Subjects: artificial neural networks; Classification; Correlation coefficients; Crops; Decision making; Discriminant analysis; Electronic noses; Growth models; Hydroponics; Infestation; Infrared spectra; Infrared spectroscopy; Insects; Learning theory; Low cost; Machine learning; Model accuracy; Modelling; Near infrared radiation; Photosynthesis; photosynthesis modeling; Physiology; plant water status modeling; Population; Remote sensing; Remote sensors; Seeds; Sensors; Spectrum analysis; Transpiration; Unmanned aerial vehicles; volatile compounds; Wheat
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21175948

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 on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.