First Report of Ralstonia pseudosolanacearum Causing Bacterial Wilt in Ginger in Peru.

Around 90% of Peru's ginger (Zingiber officinale) production is concentrated in the Junín region, due to the optimal agroecological conditions for its cultivation. In March 2024, fields with ginger plants (cultivar Criollo) in Junín region, provinces Chanchamayo and Satipo, specifically in the cities of Pichanaqui and Satipo respectively, exhibited approximately 40% of plants with severe symptoms of a disease characterized initially by plant yellowing and rapid progressing to necrosis. Affected rhizomes showed dark vascular bundles with milky white exudates upon cutting, while stems displayed vascular necrosis hindering water and nutrient transport, often resulting in plant death. Fifteen plants were sampled and diseased vascular tissues from rhizomes and stems were cultured on nutrient agar (NA) and incubated at 28°C. After 72 h, all isolations resulted in colonies with typical characteristics of Ralstonia solanacearum species complex (RSSC) were produced, with appearing fluid, irregularly round, and creamy white. Three isolates were selected for the identification steps (UNALM-RP01 to 03) were identified by PCR using primers 759/760 (Opina et al. 1997) confirming as RSSC with a 282 bp amplification product. Additionally, isolates were assigned to biovar 3 based on their ability to metabolize three acid-producing disaccharides (maltose, lactose, cellobiose) and three hexose alcohols (mannitol, sorbitol, dulcitol) (Hayward, 1964). Phylotype I was identified by multiplex PCR (primers Nmult) with a 114 bp amplification product (Fegan and Prior 2005). For the identification of the sequevars of the three isolates, DNA was extracted and PCR with primers ENDO-F/R (Ji et al. 2007) were performed to amplify and sequence the partial gene sequence of egl gene with 681 bp in length. The phylogeny by Neighbor joining with 10,000 bootstraps clustered the UNALM isolates along other sequevar 30 of R. pseudosolanacearum. The sequences were deposited in Genbank under accessions PQ213016, PQ213017 and PQ213018. For pathogenicity tests, bacterial colonies of isolate UNALM-RP01 were scraped from the culture media with a sterile needle and introduced into the stems of three 2-month-old ginger plants (cultivar Gigante). The plants subsequently exhibited yellowing seven days post-inoculation. Additionally, the rhizomes showed internal discoloration and bacterial exudation. Three plants were used as a control, which were pierced with a sterilized needle and showed no symptoms. All tested plants were kept in a greenhouse with controlled temperature (20-40 °C) The pathogen was successfully re-isolated from infected plants on NA medium, presenting typical colonies of RSSC and identified via PCR with primers 759/760, fulfilling Koch's postulates. This represents the first case in Peru of ginger plants infected with a Ralstonia species, specifically R. solanacearum phylotype I, corresponding to R. pseudosolanacearum. This species of RSSC and sequevar is known for causing disease in ginger. Its presence in Peru, however, may be the result of the pathogen's introduction, as its geographical origin is associated with Asia (Fegan and Prior 2005). To our knowledge, this is the first report of R. pseudosolanacearum causing ginger wilt disease in Peru. In 2024, an estimated average yield loss of 30% has been attributed to wilt disease in the Junín region, posing a significant threat to cultivation. Urgent and effective disease management strategies are essential to control and mitigate further losses.

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges.

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.