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Article

Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status

1
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
2
Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK S7N 0X2, Canada
*
Author to whom correspondence should be addressed.
Nitrogen 2020, 1(1), 67-80; https://doi.org/10.3390/nitrogen1010007
Received: 11 June 2020 / Revised: 30 July 2020 / Accepted: 18 August 2020 / Published: 25 August 2020
A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status. View Full-Text
Keywords: plant phenotyping; non-invasive; spectrometer; reflectance; machine learning plant phenotyping; non-invasive; spectrometer; reflectance; machine learning
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MDPI and ACS Style

Habibullah, M.; Mohebian, M.R.; Soolanayakanahally, R.; Bahar, A.N.; Vail, S.; Wahid, K.A.; Dinh, A. Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status. Nitrogen 2020, 1, 67-80. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen1010007

AMA Style

Habibullah M, Mohebian MR, Soolanayakanahally R, Bahar AN, Vail S, Wahid KA, Dinh A. Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status. Nitrogen. 2020; 1(1):67-80. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen1010007

Chicago/Turabian Style

Habibullah, Mohammad, Mohammad R. Mohebian, Raju Soolanayakanahally, Ali N. Bahar, Sally Vail, Khan A. Wahid, and Anh Dinh. 2020. "Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status" Nitrogen 1, no. 1: 67-80. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen1010007

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