Assessing Potato Yellow Vein Virus (PYVV) infection using remotely sensed data

Abstract

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.

Spectral reflectance pattern in soybean for assessing yellow mosaic disease

“…Characteristic changes in reflectance spectrum has been observed due to Grapevine leafroll associated virus-3 in grape [23], Sugarcane yellow leaf virus in sugarcane [8], Potato yellow vein virus infection in potato [7] and viral infection in Nicotiana debneyi [26]. Though, commonly used broadband have been shown to detect differences between healthy and diseased plants [16,24,32], but discrimination of healthy plants from those showing mild symptom is not very sharp.…”

“…Use of hyerspectral remote sensing techniques and studies [21,39] has indicated the immense potentiality of hyperspectral imaging for discrimination of disease and other plant stresses and early their detection [3,38]. A few studies have demonstrated the feasibility of using remote sensing data for detection of virus diseases like grapevine leaf roll [23], tobacco mosaic [26], sugarcane yellow leaf [8], barley yellow dwarf and wheat streak mosaic [29,40], potato yellow vein [7] and mungbean yellow mosaic [27]. For remote sensing detection specific spectral reflectance or ratio associated with yellow mosaic infection is required for large scale assessment and monitoring of yellow mosaic disease in soybean field especially for strategic or tactical crop management decision and yield loss prediction.…”

Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data

“…Hence, the objective of this work was to test the feasibility of using the multispectral light reflectance of plants, supported by conventional and wavelet-based multifractal analyses of the reflectance signal, for detecting R. solanacearum infection in potato crops, aiming at developing a practical field monitoring method for the spatial assessment of the health condition of the crop. Although this work is methodologically similar to that reported in Chávez et al (2009Chávez et al ( , 2010, it is different in the sense that it tries to look for a general application of the diagnostic tool to different stresses which generally cause different physiological and morphological changes in affected plants. The general hypothesis is that different plant reactions, caused by a diversity of stressors (e.g.…”

“…A method previously used for virus (PYVV) detection in potato (Chávez et al 2009(Chávez et al , 2010 was slightly modified by splitting the reflection spectra into 4 discrete bands to mimic those of the Landsat TM: blue (450-520 nm), green (520-600 nm), red (630-690 nm) and NIR (760-900 nm). The proportion of the reflected radiation per band relative to the total of the four bands was calculated as a function of time (growth), resulting in a heterogeneous reflectance spectra displaying anomalies through time.…”

“…Using this method, Chiwaki et al (2005) were able to determine bacterial infection in plants 4 days earlier than the visual identification of symptoms, as the invasion of vascular bundles by multiplying bacteria reduced transpiration and caused a raised leaf temperature. Another promising technique is remote sensing and spectroradiometry of plant reflectance as it allows the early detection of morphological and anatomical changes even if these changes are small, as shown for virus infection (Chávez et al 2009(Chávez et al , 2010. Our own unpublished work provides evidence that the technique is also able to detect changes caused by water stress in plants.…”