Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms

• Main Authors: Jan Behmann, David Bohnenkamp, Stefan Paulus, Anne-Katrin Mahlein
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Journal of Imaging
• Subjects: hyperspectral imaging; plant phenotyping; disease detection; spectral tracking; time series
• Online Access: https://www.mdpi.com/2313-433X/4/12/143

The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400⁻1000 nm) hyperspectral camera.