The primary objective of soybean-breeding programs is to develop cultivars that offer both high grain yield and a maturity cycle tailored to the specific soil and climatic conditions of their cultivation. Therefore, predicting the genetic value is essential for selecting and advancing promising genotypes. Among the various analytical approaches available, deep machine learning emerges as a promising choice due to its capability to predict the genetic component of phenotypes assessed under field conditions, thereby enhancing the precision of breeding decisions. This study aimed to determine the efficiency of artificial neural networks (ANNs) in predicting the genetic values of soybean genotypes belonging to populations derived from crosses between parents of different relative maturity groups (RMGs). We characterized populations with broad and restricted genetic bases for RMG traits. Data from three soybean populations, evaluated over three different agricultural years, were used. Genetic values were predicted using the multilayer perceptron (MLP) artificial neural network and compared to those obtained using the best unbiased linear prediction from variance components using restricted maximum likelihood (RR-BLUP). The MLP neural network efficiently predicted genetic values for the relative maturity group trait for genotypes belonging to populations of broad and restricted crosses, with an R² of 0.999 and root-mean-square error (RMSE) of 0.241, and for grain yield, there was an R² of 0.999 and an RMSE of 0.076. While the percentage of coincident superior genotypes remained relatively consistent, a significant difference was observed in their ranking order. The genetic gain with selection estimated using MLP was higher by 30–110% compared to RR-BLUP for the relative maturity group trait and 90–500% for grain yield. Artificial neural networks (ANNs) showed higher efficiency than RR-BLUP in predicting the genetic values of the soybean population. Local selection at intermediate latitudes is conducive to developing lines adaptable for regions at higher and lower latitudes.

This study aimed to identify promising soybean lines by combining genetic parameter estimates, simultaneous selection for grain yield and oil content, and a complementary multivariate classification of already phenotyped materials. Four soybean populations were evaluated in Jaboticabal, São Paulo, Brazil, across three agricultural years corresponding to the F5:6, F5:7, and F5:8 generations. The breeding populations comprised 142 advanced lines, whereas the augmented-block field experiment contained 281 plot-level experimental records in each agricultural year because the complete layout also included the repeated check plots required by the design. Population-based analyses were conducted using REML/BLUP, likelihood-ratio tests, and a standardized Z selection index for grain yield and oil content. The Z index identified the highest-ranked lines within each population, including genotypes 19, 42, 38, and 27 in populations A, B, C, and D, respectively. A complementary Random Forest analysis was used as a decision-support classification procedure applied to observed phenotypic information, rather than as a model for forecasting future performance or estimating unknown oil or protein values. The procedure organized the phenotypic profiles into oil-based, protein-based, and combined oil-plus-protein groups. For the combined scenario, the classification criterion rewarded high standardized values for both seed-composition traits while penalizing imbalance between them. Out-of-fold classification results showed high agreement with the predefined operational groups, with overall accuracy rates of 0.99, 1.00, and 0.98 for the oil, protein, and combined scenarios, respectively. Principal component analysis described the multivariate structure of the same phenotypic dataset. Because generation and crop year coincide, changes in genetic parameter estimates across successive evaluations cannot be attributed exclusively to selection. Overall, the results support the use of mixed models and the Z index for line selection, while Random Forest provides a complementary framework for organizing already measured materials according to contrasting and combined seed-composition objectives.

Charcoal rot, a destructive disease of soybean roots, has limited control options, and the development of resistant cultivars is the most effective, economical, and sustainable strategy. However, tropical cultivars are generally susceptible, while exotic resistant accessions are poorly adapted to tropical conditions. This study represents the initial step toward developing tropical soybean cultivars combining resistance and agronomic performance. We evaluated the agronomic performance of progenies derived from crosses between exotic lines partially resistant to Macrophomina phaseolina (PI 594302 and PI 567562A) and three commercial tropical cultivars with high yield potential (relative maturity groups (RMGs) 5.5, 6.2, and 6.9). Populations were advanced to the F₆ generation and assessed for yield, plant height, first pod insertion, and agronomic value using a partial diallel design. Grain yield ranged from 18.5 to 33.2 g·plant⁻¹ across generations. The combining ability analysis revealed predominance of additive genetic effects, highlighting PI 594302 and VMAX RR (RMG 6.2) as key parents for plant height, agronomic value, and grain yield and exhibited the greatest potential for superior progeny across generations. These results identify promising lines with high agronomic performance and adaptation, which will undergo resistance confirmation in subsequent studies, supporting the long-term goal of developing tropical soybean cultivars that combine productivity, sustainability, and tolerance to charcoal rot.