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Ralstonia solanacearum bacterial wilt is a major banana crop disease that generates high worldwide losses. An effective management practice for this disease has not been developed at this time, so the main strategy to control it involves early detection of infected plants. This research has characterized the responses of healthy plants infected with R. solanacearum and subjected to hydricstress, in two banana cultivars by spectroscopy reflectance. Additionally, this input was used to detect vascular wilt during the incubation period of the disease in the Grosh Michel and Williams banana varieties. Reflectance data was collected from plant leaves disposed in a completely randomized design using a portable spectrometer and subjected to the following treatments: 1) 30 control plants var. Gros Michel, 2) 30 plants var. Gros Michel inoculated with R. solanacearum, 3) 30 plants var. Gros Michel subjected to hydricstress, 4) 30 control plants var. Williams, 5) 30 plants var. Williams inoculated with R. solanacearum, and 6) 30 plants var. Williams under hydricstress. Both varieties of healthy plants showed a typical low reflectance in the visible range, with a peak of green of around 12% in the var. Williams and 5% in the var. Grosh Michel. In the measured infrared range (700-100nm), the two varieties presented values between 60 -70% during the longest time of the measurements, decreasing to ˜50% after 12 DPI. The obtained results indicate that in banana plants infected with R. solanacearum, there is no initial increase for reflectance in the Vis range, as occurs in other pathosystems, whereas reflectance decreases rapidly after 6 DPI in the 700-1000 nm range. This methodology identifies three wavelengths (710 nm, 770 nm, and 965 nm) that allow differentiation between plants inoculated with R. Solanacearum and healthy plants after 6 inoculation days, with percentage ranges of correct classification from 96 to 100%. Spectral response characterization in healthy plants and those subjected to various types of stress is a fundamental input for the development of early disease detection systems based on spectroscopy techniques, which can help prevent disease spread and reduce crop losses.