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

Abstract

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.

Investigating the Utility of Oblique Tree-Based Ensembles for the Classification of Hyperspectral Data

“…For example, the visible-near infrared (VNIR) spectrum has been particularly useful for the detection of stress in agricultural crops. Chávez et al [7], used the 350 nm to 850 nm spectral range to detect bacterial wilt infection caused by Ralstonia solanacearum in potato crops. Similarly, [8] employed leaf and canopy VNIR reflectance data (325 nm to 1075 nm) to detect damage in rice crops caused by Cnaphalocrocis medinalis .…”

Efficacy of PCR-bassed techniqes and remote sensing technology for detecting potato brown rot infection caused by R. solanacearum

“…Any stress on the plant leads to different physiological and morphological changes in affected plants and remote sensing technique can detect early morphological and anatomical changes even if these changes are small (Cha´vez et al 2009(Cha´vez et al , 2010. Moreover, Cha´vez et al, (2012) noted that different plant reactions, caused by different factors (e.g. bacteria, virus, water deficits, meterological aspects) may be well detected by test sensing devices and analytical ways.…”

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

“…On the contrary, the discrimination of powdery mildew and leaf rust in winter wheat was achieved as early as one and two days post inoculation using blue-green and chlorophyll fluorescent [81]. Reflectance in the 350 -850 nm region of the spectrum was found to be useful for distinguishing bacterial wilt (Ralstonia solanacearum) infected potato (Solanum tuberosum) at the same time as the expert trained eye in 100% of the observations made [27]. Another recent study indicated that remotely sensed data can identify Rhizoctonia crown and root rot caused by Rhizoctonia solani in sugar beet [82].…”

“…Results from greenhouse and field studies have shown that it is possible to observe and quantify changes in crop health using remote sensing over the course of a growing season [27][28][29][30][31][32][33]. The presence of a disease or insect feeding leads to reductions in green leaf area and chemical and pigment concentrations, and alteration of cell structure, water and nutrient uptake, and gas exchange, which is known to modify canopy reflectance characteristics.…”