Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data
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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.
KeywordsRemote sensing diagnostic method Visual monitoring Multispectral analysis Wavelet transform Precision agriculture
Support for this work was provided by the International Foundation for Science (IFS Grant 4068/-I), the Production Systems and the Environment Division of the International Potato Center (CIP) and the CIP-ALTAGRO project. The authors thank Eng. Liliam Gutarra from the Integrated Crop Management Division at CIP for her support on laboratory assessments, and to R.T.J. McAteer and collaborators for kindly sharing their wavelet-multifractal algorithm. P. Chávez gives special thanks to Arnauld A. Thiry for his permanent and unconditional support, and Drs. Salomón Helfgott and Vicente Rázuri from La Molina Agricultural University for their good advices.
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