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This study used greenhouse conditions to determine the hyperspectral responses of cape gooseberry (Physalis peruviana L.) plants inoculated with different Fusarium oxysporum f. sp. physali densities because the causal agent of vascular wilt generates great economic losses for farmers. A completely randomized design with four replicates was established. The evaluated treatments were inoculum densities 0.0, 1.0·103 and 1.0·106 conidia/mL of the pathogen. The inoculation was done with immersion of roots in conidia suspensions. The spectral response was directly measured on the plant leaves with a spectroradiometer. Non-invasive detection in the P. peruviana F. oxysporum pathosystem with reflectance values was used with different spectral indices related to the visible and Red Edge, which were calculated and correlated with the disease variables. The treatments showed significant differences in the visible spectrum starting 14 days after inoculation with higher reflectance values. The chlorophyll index at the red edge (ChRE), the modified chlorophyll absorption index (MCARI), the simple ratio index (SR) and the Zarco & Miller index (ZM) showed highly significant correlations with the area under the disease progress curve for leaves (AUDPCL), leaf area and fresh weight 1 Universidad Nacional de Colombia, Faculty of Agricultural Sciences, Program in Geomatics, Bogota (Colombia). ORCID Giraldo-Betancourt, C.: 0000-0002-6917-4979; ORCID Velandia-Sánchez, E.A.: 0000-0002-4324-043X 2 Independent consulter, Emeritus researcher of Colciencias, Bogota (Colombia). ORCID Fischer, G.: 0000-0001-8101-0507 3 Universidad Nacional de Colombia, Faculty of Agricultural Sciences, Bogota (Colombia). ORCID Gómez-Caro, S.: 0000-0002-6670-0275; ORCID Martínez, L.J.: 0000-0001-9010-9189 4 Corresponding author. sgomezc@unal.edu.co The cultivation of cape gooseberry (Physalis peruviana L.) is of great economic importance for Colombia since this species is the third most exported fruit, with a production of 16,109 t on 1,312 ha in 2018 (Agronet, 2020). This fruit is desirable because of its nutritional and medicinal characteristics, which make it an exotic fruit (Fischer and Melgarejo, 2020). Although most cape gooseberry crops belong to small farmers, export companies have decided to plant their own crops to obtain constant production to meet the market demands of this fruit (Fischer et al., 2014). Fusarium oxysporum is the causal agent of vascular wilt in plants, and, in cape gooseberries, the incidence of this disease, caused by F. oxysporum f. sp. physali (Simbaqueba et al., 2018), generates great economic losses for farmers. Therefore, new pathogen-free areas are required (Villarreal-Navarrete et al., 2017). In 2014, F. oxysporum caused the greatest amount of economic damage to several agricultural crops when compared to other plant pathogens because of its large spatial distribution in Europe, the Middle East, North Africa (Shabani et al., 2014), the United States, Canada, Australia, Thailand, China, Colombia, Ecuador, Costa Rica, Panama, Brazil, Peru, and Argentina, especially in banana (Musa acuminata), tomato (Solanum lycopersicum) (McGovern, 2015), onion (Allium cepa) (Southwood et al., 2015) and ornamental crops (Gullino et al., 2015; Lecomte et al., 2016). In Colombia, the planted area for cape gooseberry was 1,087 ha by 2009, with a yield of 17.78 t ha-1; however, in 2010, there was a decrease of 342.1 ha (Agronet, 2020) because of inadequate crop management, which generated a greater incidence and severity of vascular wilt caused by F. oxysporum f. sp. physali. F. oxysporum can survive up to 30 years under adverse conditions because of its particular survival of the aerial part of the plants. This study showed the potential of spectral patterns for the detection and study of Fusarium wilt in P. peruviana. Additional keywords: reflectance; spectral indices; phytopathological response; vascular wilt.

Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection