Abstract: The thermoinhibition of lettuce seed germination causes important losses for producers, who do not have thermotolerant commercial cultivars. One of the obstacles has been the scarcity of optimizing techniques capable of efficiently discriminating thermotolerant and thermosensitive cultivars. The aim of this work was to evaluate the use of neural networks to discriminate different levels of thermoinhibition in lettuce seeds. Seeds of 18 cultivars were evaluated for thermoinhibition considering the characteristics of the first and last germination count and germination speed index, in seeds subjected to temperatures of 20, 25, 30 and 35 °C. The remaining seeds, which did not germinate, were subjected to the tetrazolium test. Analyses were performed immediately after seed harvesting and repeated after six months of storage. Discriminant analysis was performed and the Kohonen’s Self-Organizing Map (SOM) was created using Artificial Neural Networks (ANNs). Neural networks discriminate lettuce cultivars and organizes them in terms of seed thermoinhibition tolerance through Kohonen’s Self-Organizing Map. Discriminant analysis consistently identifies the Everglades and Luiza genotypes as tolerant to thermoinhibition.

Resumo: A termoinibição causa perdas importantes para os produtores, os quais não dispõem de cultivares comerciais com sementes termotolerantes. Há escassez de técnicas otimizadoras capazes de discriminar cultivares termotolerantes e termosensíveis com eficiência. Objetivou-se avaliar o uso de redes neurais para discriminar diferentes níveis de termoinibição em sementes de alface. Foram avaliadas sementes de 18 cultivares quanto à termoinibição considerando às características de primeira e última contagem de germinação e índice de velocidade de germinação, em sementes submetidas às temperaturas de 20, 25, 30 e 35 °C. As sementes remanescentes, que não germinaram, foram submetidas ao teste de tetrazólio. As análises foram realizadas imediatamente após a colheita das sementes e repetidas após seis meses de armazenamento. Uma análise discriminante e o Mapa Auto-Organizável de Kohonen (SOM) por Redes Neurais Artificiais (RNA’s) foram realizados. As redes neurais discriminam as cultivares de alface e as organiza quanto a tolerância a termoinibição das sementes por meio do Mapa Auto-Organizável de Kohonen. A análise discriminante indentifica de maneira coerente o genótipo Everglades e Luiza como tolerantes a termoinibição.

ABSTRACT. Cotton (Gossypium hirsutum L.) is the world’s leading natural textile fibre and is grown in over 60 countries, including Brazil, where it is an important agricultural commodity. The cultivation area currently covers approximately one million hectares in Brazil and has expanded into every region of the country, especially the Cerrado biome. Because of this expansion, it is necessary to analyse the influence of the environment on the genotype behaviour to optimize yields. Thus, the objective of this study was to compare fuzzy logic to traditional methods for selecting coloured-fibre cotton genotypes with high adaptability and yield stability. The experiment was conducted on the 2013/2014, 2014/2015, 2015/2016, and 2016/2017 crops of the Capim Branco farm at the Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil. The following methods were used to select genotypes for adaptability and stability: the Lin and Binns model, additive main effects and multiplicative interaction (AMMI) analysis and the Sugeno fuzzy logic controller. An interaction of the genotype with the environment that affected yield was detected. Environment 4 (the 2016/2017 crop) showed to the lowest genotype to environment interaction. The fuzzy logic approach showed agreement with AMMI and the nonparametric Lin and Binns method. The linguistic fuzzy logic used in the Sugeno fuzzy logic controller demonstrated the potential for selecting cotton genotypes in plant breeding programmes. The UFUJP-16 and UFUPJ-17 genotypes were adaptable, stable and showed promising yields within the tested environments. The fuzzy logic method was effective for estimating adaptability and stability.

ABSTRACT A better understanding of genetic diversity in cotton cultivars is needed to make the most of the genetic resources and guarantee continuing improvements in the breeding programs. Our objective was to evaluate the genetic diversity among cotton genotypes from the top breeding programs in Brazil and recommend hybrid combinations that would increase variability. The field experiments we conducted on three cotton genotypes from each of the following Brazilian breeding programs: EMBRAPA, MONSANTO DELTAPINE, FIBERMAX, IMA, TMG and UFU. Several phenotypic evaluations were carried out at V5, B1, full flower and full maturity. Moderate genetic divergence was observed among the genotypes from these breeding programs. Hybridization of the BRS 433 FL B2RF and FM 980 GLT genotypes would likely produce segregating populations with greater genetic variability, yield potential, lint yield and desirable fiber quality. Maturity, micronaire and fiber length were the strongest contributors to genetic divergence.

Resumo: O objetivo deste trabalho foi avaliar a eficácia dos métodos “straw test” e imersão em ácido oxálico na identificação dos níveis de resistência do algodoeiro (Gossypium hirsutum) branco e colorido ao mofo-branco (Sclerotinia sclerotiorum). Foram avaliados dez genótipos: cinco de fibra colorida, quatro de fibra branca e um genótipo suscetível de fibra branca. Os genótipos MAB-1 de fibra branca e MAC-2 de fibra colorida foram os mais resistentes ao mofo-branco de acordo com os métodos imersão em ácido oxálico e straw test, respectivamente. Esses genótipos podem ser indicados como fontes de resistência para programas de melhoramento. Ambos os métodos analisados são complementares entre si; no entanto, o straw test é mais eficiente na avaliação da resistência dos genótipos de algodão ao mofo-branco.

Abstract: The objective of this work was to evaluate the efficiency of the straw and oxalic acid tests to identify resistance levels of white and colored cotton (Gossypium hirsutum) genotypes to white mold (Sclerotinia sclerotiorum). Ten genotypes were evaluated: five with colored fiber, four with white fiber, and a white-fiber susceptible genotype. The genotypes MAB-1 with white fiber and MAC-2 with colored fiber were the most resistant to white mold, according to the immersion in oxalic acid and straw tests, respectively. These genotypes can be recommended as resistance sources for breeding programs. Both assessed tests are complementary to each other; however, the straw test is more efficient in evaluating the resistance of cotton genotypes to white mold.