Coping with chemical complexity in mammal-plant interactions: near-infrared spectroscopy as a predictor of Eucalyptus foliar nutrients and of the feeding rates of folivorous marsupials

Oecologia ◽  
2001 ◽  
Vol 128 (4) ◽  
pp. 539-548 ◽  
Author(s):  
Allen M. McIlwee ◽  
Ivan R. Lawler ◽  
Steven J. Cork ◽  
William J. Foley
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Foliar Nutrients ◽  
Plant Interactions ◽  
Feeding Rates ◽  
Chemical Complexity

scholarly journals 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

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5948
Author(s):  
Sigfredo Fuentes ◽  
Eden Tongson ◽  
Ranjith R. Unnithan ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Management Practices ◽  
Near Infrared ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Plant Interactions ◽  
Aphid Infestation

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.

Near-infrared spectroscopy (NIRS) – a promising brain imaging method in Psychiatry?

2008 ◽  
Vol 39 (01) ◽  
Author(s):  
AJ Fallgatter ◽  
AC Ehlis ◽  
MM Richter ◽  
M Schecklmann ◽  
MM Plichta
Keyword(s):  
Infrared Spectroscopy ◽  
Brain Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Imaging Method

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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