A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops

• Main Authors: Mohammad Habibullah, Mohammad Reza Mohebian, Raju Soolanayakanahally, Khan A. Wahid, Anh Dinh
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Sensors
• Subjects: non-invasive; machine learning; leaf nitrogen; reflectance; plant phenotyping
• Online Access: https://www.mdpi.com/1424-8220/20/5/1449

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&#8212;canola, corn, soybean, and wheat&#8212;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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>) 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 <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> of 18.02%, corn showed an <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> of 68.41%, soybean showed an <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> of 46.38%, and wheat showed an <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> 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.