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Leaf scald caused by Xanthomonas albilineans in sugarcane is one of the most important diseases around the world since it causes severe losses in both agricultural and industrial yields. In Cuba, resistance to this disease is considered a major selection criterion within the breeding program. The aim of this work was to detect sugarcane cultivars with stable reaction to X. albilineans by using both additive main effects and multiplicative interaction (AMMI) and linear general models. For this, 16 cultivars planted simultaneously in 2015, 2016, and 2017 in two locations: Jovellanos (Matanzas) and Florida (Camagüey) in Cuba were mechanically inoculated with a bacterial isolate previously characterized by sequencing its DNA fragment. Disease severity was recorded in plant cane and first ratoon, and results were consistent in both analyses and revealed that L55-5 and C323-68 were the most susceptible cultivars and C1051-73 was the most resistant; however, C1051-73, C275-80, C86-12, C88-382, C89-147, My5514, and Ty86-28 were the most stable across the years and localities evaluated. Results will allow adapting the methodology for the evaluation of the reaction to leaf scald of new sugarcane cultivars.

Image analysis provides an accurate and precise method of pest evaluation. This work's objective was to compare the usefulness of the ImageJ® 1.43u image processor and visual estimation as methods to characterize brown rust lesions and estimate the resistance of new sugarcane cultivars. For this, leaves images of 10 cultivars were captured, and the parameters quantity, most regular size of the pustules, and leaf area affected were determined. The data were correlated with the eight control (standard) genotypes' evaluations to obtain a classification of disease resistance. The results showed that the software's determinations were the most accurate, although all the methods were reliable for rating the reaction to brown rust. Therefore, it is proposed to move away from visual disease assessment toward a system based on digital image analysis.