Applying Multifractal Analysis to Remotely Sensed Data for Assessing PYVV Infection in Potato (Solanum tuberosum L.) Crops

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Abstract
Multispectral reflectance imagery and spectroradiometry can be used to detect stresses affecting crops. Previously, we have shown that changes in spectral reflectance and vegetation indices detected viral infection 14 days before visual symptoms were noticed by the trained eye. Herein we present evidence that shows that the application of multifractal analysis and wavelet transform to spectroradiometrical data improves the diagnostic power of the remote sensing-based methodology proposed in our previous work. The diagnosis of viral infection was effectively enhanced, providing the earliest detection ever reported, as anomalies were detected 29 and 33 days before appearance of visual symptoms in two experiments.

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