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A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops.
Habibullah, Mohammad; Mohebian, Mohammad Reza; Soolanayakanahally, Raju; Wahid, Khan A; Dinh, Anh.
Afiliación
  • Habibullah M; Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
  • Mohebian MR; Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
  • Soolanayakanahally R; Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK S7N 0X2, Canada.
  • Wahid KA; Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
  • Dinh A; Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
Sensors (Basel) ; 20(5)2020 Mar 06.
Article en En | MEDLINE | ID: mdl-32155829
Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants-canola, corn, soybean, and wheat-are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination () of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an of 18.02%, corn showed an of 68.41%, soybean showed an of 46.38%, and wheat showed an of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Agua / Técnicas Biosensibles / Análisis Costo-Beneficio / Hojas de la Planta / Productos Agrícolas / Dispositivos Ópticos / Nitrógeno Tipo de estudio: Health_economic_evaluation Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Agua / Técnicas Biosensibles / Análisis Costo-Beneficio / Hojas de la Planta / Productos Agrícolas / Dispositivos Ópticos / Nitrógeno Tipo de estudio: Health_economic_evaluation Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article