Neural networks applied to plant breeding for predicting grain yield in common bean genotypes

Common bean is a crop of great socioeconomic importance for several developing countries, being fundamental to food security. Therefore, identifying excellent genotypes for grain yield is a critical step in breeding programs. This study aimed to evaluate the performance of artificial neural networks in predicting common bean genotypes considering different grain yield ranges, using data obtained from multiple environments. The model was trained and tested with phenotypic variables to classify genotypes into grain yield categories (poor, medium, good, and excellent). The results indicated satisfactory performance, with an overall accuracy of 70%, as well as a higher discriminative capacity in the extreme classes, especially “excellent” and “poor.” The area under the curve reinforced the model’s effectiveness, with values above 0.90 for the “excellent” and “poor” classes, which are considered priorities in genotype recommendation and discard processes. Therefore, the use of neural networks proved to be a promising tool to support decision-making in common bean breeding, allowing efficient identification of promising genotypes and elimination of less productive ones during the final field evaluation stage.