Banana Xanthomonas wilt, caused by Xanthomonas vasicola pv. musacearum (Xvm), is a devastating disease that results in total yield loss of affected plants. Resistance to the disease is limited in Musa acuminata, but it has been identified so far in the zebrina subspecies. This study identified markers associated with tolerance to Xvm in Monyet, a tetraploid banana from the zebrina subspecies which was identified to be partially resistant to the bacterium. We used a triploid progeny of 135 F₁ hybrids resulting from a cross between Monyet (Xvm partially resistant) and Kokopo (diploid and Xvm susceptible). The F₁ hybrids were screened in pots for resistance to Xvm. The population was genotyped using the genotyping-by-sequencing platform of Diversity Array Technology (DArTSeq). The adjusted means of the phenotypic data were combined with the allele frequencies of the genotypic data in continuous mapping. We identified 25 SNPs associated with resistance to Xvm, and these were grouped into five quantitative traits loci (QTL) on chromosomes 2, 3, 6, and 7. For each marker, we identified the favorable allele and the additive effect of replacing the reference allele with the alternative allele. The comparison between weevil borer (Cosmopolites sordidus (Germar)) and Xvm QTL revealed one QTL shared between the two biotic stresses at the distal end of chromosome 6 but with a repulsion linkage. This linkage should be broken down by generating more recombinants in the region. We also identified 18 putative alleles in the vicinity of the SNPs associated with resistance to Xvm. Among the 18 putative genes, two particularly putative genes, namely, Ma06_g13550 and Ma06_g36840, are most likely linked to disease resistance. This study is a basis for marker-assisted selection to improve banana resistance to banana Xanthomonas wilt, especially in East and Central Africa where the disease is still devastating the crop.

Accurately assessing weevil damage is critical when evaluating banana germplasm to identify genotypes resistant to the banana weevil (Cosmopolites sordidus), for use as elite parents in the banana breeding pipeline or evaluating breeding products. Visual observation remains the most common phenotyping approach but limited by individual bias. This study investigated the potential of image analyses as precise and objective alternatives for assessing weevil damage on the banana corm. Phenotyping trials were set up as partially replicated (P-rep) designs with 22 tissue culture-generated genotypes raised in pots and infested with banana weevils. At termination, the percentage score of weevil damage to the corms was evaluated by visual observation and image analysis using ImageJ and machine learning. In total, 370 high-quality images were assessed for weevil damage using ImageJ and machine learning. On average, damage scores from visual observation were 5.52% and 3.88% higher than ImageJ and machine learning respectively. There was a proportional trend with visual observation agreeing closely to image analyses for smaller scores and but less for larger scores. The results show that both ImageJ and machine learning exhibited a strong level of agreement and are interchangeable with consistent, reliable, and repeatable measurements. In conclusion, to avoid individual bias and subjectivity arising from visual observation, we recommend the use of either ImageJ or machine learning when scoring weevil damage in the banana corm.