Potato Yellow Vein Virus (PYVV) threatens potato production in South America. Visual field monitoring is commonly used to detect PYVV on potato crops but the disease is generally detected only after significant damage has occurred to photosynthetic tissues. Therefore, a method for detecting the disease before yields are severely affected would be useful. Remotely sensed multispectral reflectance, based on the reflectivity and propagation of light radiation inside plant tissues, was tested for the detection of PYVV infection in potato plants grown indoors. A visual assessment of disease symptoms in both virus-infected and virus-free plants was compared to monitoring based on spectroradiometry and multispectral photographic images of the same plants, recorded during their growth and development. Results showed that changes in reflectance in certain regions of the electromagnetic spectrum, indicative of disturbances in light reflection by vascular tissues in infected plants, measured with an spectroradiometer, as well as derived spectral Vegetation Indices such as NDVI, SAVI, and IPVI, provide early detection of viral infection, long before symptoms of chlorosis can be detected by the trained eye.

Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease.

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