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The selection indexes aggregate information to multiple characters and, with this, they are able to carry out the selection of a set of variables simultaneously. The objective was to verify the genetic potential of agronomic traits and to select soybean F3:4 progenies based on different selection strategies. 123 progenies and the parents were sown in randomized blocks with two replications. The gains of direct selection by the indexes, the sum of “ranks” and the genotype-ideotype were lower for all characters when compared to the gains of direct and indirect selection. The rank sum index stood out for achieving the highest total gain with 37.11%. The index of the genotype-ideotype obtained a lower gain (-0.48%) for the character number of days for flowering compared to the sum index of “ranks” (-0.54%) and reached a negative gain for the attribute insertion height of the first pod with -1.82%. The genetic potential of the F3:4 population is high and allows different selection strategies to be applied to reach superior genotypes. The progenies UFU 72, UFU 116, UFU 86, UFU 45, UFU 117, UFU 56, UFU 5, UFU 106, UFU 6, UFU 4, UFU 73, UFU 101, UFU 96, UFU 90, UFU 123, UFU 116, UFU 88, UFU 65, UFU 70, UFU 3, UFU 69 and UFU 37 were selected by both selection indexes. The UFU 72, UFU 90, UFU 88 and UFU 69 progenies are agronomically superior both in direct and indirect selection, as in Mulamba and Mock (1978) sum of “ranks” selections and genotype-ideotype. RESUMO Os índices de seleção agregam informações de múltiplos caracteres e, com isto, conseguem realizar a seleção simultaneamente de um conjunto de variáveis. Objetivou-se verificar o potencial genético de caracteres agronômicos e selecionar progênies F3:4 de soja com base em diferentes estratégias de seleção. Foram semeadas 123 progênies e os genitores, em blocos casualizados com duas repetições. Os ganhos de seleção direta pelos índices, soma de “ranks” e do genótipo-ideótipo, foram inferiores para todos os caracteres quando, comparados aos ganhos da seleção direta e indireta. O índice de soma de ranks se destacou por alcançar o maior ganho total com 37,11%. O índice do genótipo-ideótipo obteve menor ganho (-0,48%) para o caráter número de dias para o florescimento comparado ao índice de soma de “ranks” (-0,54%) e, alcançou ganho negativo para o atributo altura de inserção da primeira vagem com -1,82%. O potencial genético da população F3:4 é elevado e permite aplicar distintas estratégias de seleção no alcance de genótipos superiores. As progênies UFU 72, UFU 116, UFU 86, UFU 45, UFU 117, UFU 56, UFU 5, UFU 106, UFU 6, UFU 4, UFU 73, UFU 101, UFU 96, UFU 90, UFU 123, UFU 116, UFU 88, UFU 65, UFU 70, UFU 3, UFU 69 e UFU 37 foram selecionadas por ambos índices de seleção. As progênies UFU 72, UFU 90, UFU 88 e UFU 69 são agronomicamente superiores tanto na seleção direta e indireta, quanto nos índices de seleção de soma de “ranks” de Mulamba and Mock (1978) e do genótipo-ideótipo.

The development of soybean cultivars resistant to Phakopsora pachyrhizi (also known as Asian Soybean Rust (ASR)) may be achieved through genetic breeding. In the stage of selecting the resistant genotypes, one factor that can be used is to observe visually the severity of the rust symptoms upon leaflets of sampled soybean plants. By being able to quantify the exact amounts of regions with chlorosis, necrosis, and unaffected of each sample an automatic method is developed to grade rust severity and to classify the samples accordingly. The proposed approach uses input images of soybean leaflets, in a range from healthy to different levels of ASR severity, sampled from controlled experiments in a farm in southeast Brazil. Images are then processed by channel transformation (RGB to CIELab), channel distribution analysis, and a clustering algorithm in order to segment the regions in three main areas. Those regions represent the main visual symptoms of the ASR severity, but still with ill-defined transitions between them. A post-processing superpixel algorithm is leveraged upon those results to arrive at a first rust intervals classification. A necrosis’ neighborhood analysis is proposed to compute the intermediate areas and to include them in a final rust index. Results are given upon four different experiments: 1) 200 acquired image samples taken directly from the field; 2) 67 detached leaflets at different evolved periods of infection; 3) 50 images from a previous open database, and 4) 10 images with the standards for diagrammatic scales. By grading rust severity, and separating chlorosis, and necrosis indexes this solution contributes to efforts in managing ASR in crops, and in breeding programs.