Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

• Main Authors: Kang Yu, Georg Leufen, Mauricio Hunsche, Georg Noga, Xinping Chen, Georg Bareth
• Format: Article
• Language: English
• Published: MDPI AG 2013-12-01
• Series: Remote Sensing
• Subjects: cereal disease; barley; leaf chlorophyll concentration; blue to far-red fluorescence ratio; reflectance indices; precision agriculture
• Online Access: http://www.mdpi.com/2072-4292/6/1/64

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R2 of 0.72 and 0.79. Partial least squares (PLS) and support vector machines (SVM) regression models further improved the use of fluorescence signals for the estimation of LCC, yielding R2 of 0.81 and 0.84, respectively. Our results demonstrate that non-destructive spectral measurements are able to detect mild disease symptoms before significant losses in LCC due to diseases under natural conditions.