Recursos genéticos y mejoramiento de frutales andinos: una visión conceptual

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

Preliminary assessment of COSII gene diversity in lulo and a relative species: initial identification of genes potentially associated with domestication

Among the Solanum genus, Solanum quitoense Lam. (lulo) is a promising species of Neotropical Solanaceae to become a premium crop in international markets. Wild relatives of S. quitoense are a source of desirable characteristics to be exploited for genetic improvement. To enhance the understanding of and access to the genetic diversity in landrace and wild relatives of lulo, we estimated the relative sequence diversity among them and their wild relative Solanum hirtum. With the use of COSII markers, we established that diversity of cultivated lulo (S. quitoense) is significantly lower than that of its wild relative S. hirtum. In the same way, we found that diversity of lulo is similar to that previously reported for tomato, while the diversity of S. hirtum is comparable to that of other wild relatives of cultivated plants. Our results suggest that high variability of some genes associated to abiotic stress response and pathogen resistance has been favored in wild and cultivated lulo plants.