https://repositorio.ufu.br/handle/123456789/30498 2026-10-12T01:13:04Z https://repositorio.ufu.br/handle/123456789/50502 Title: Morphoagronomic characterization and image-based herbivory analysis for pest resistance in dwarf tomato using artificial neural networks Abstract: Dwarf tomato plants with optimized combining ability offer distinct advantages for hybrid development, including superior agronomic performance, fruit quality, and pest resistance. However, Santa Cruz-type dwarf tomato germplasm remains undercharacterized. This study evaluated morphological traits on seven populations of Santa Cruz-type dwarf tomatoes, the donor parent UFU MC TOM 1, the recurrent parent UFU-TOM-MOTHER-2, and the control cultivar Caniati. Additionally, the wild accession Solanum pennellii was included to quantify acylsugar content. Pest resistance was evaluated through acylsugar quantification and analyses of herbivory caused by Tuta absoluta and Liriomyza huidobrensis. Populations UFU-Sci#6.12-2.1.5, UFU-Sci#6.12-2.1.7, and UFU-Sc#8.3.2-2.1.2 emerged as superior, exhibiting elevated acylsugar levels, reduced pest damage, and improved fruit quality. Importantly, acylsugar content was strongly associated with increased pest resistance, particularly against T. absoluta and L. huidobrensis, mimicking the defensive profile of the wild S. pennellii accession. These findings support the development of pest-resilient, productive tomato cultivars aligned with sustainable agriculture goals. 2025-04-08T00:00:00Z https://repositorio.ufu.br/handle/123456789/50282 Title: Caracterização in silico de STRs em genomas de Aspergillus niger visando ao desenvolvimento de marcadores moleculares para cepas de bioinsumos agrícolas Abstract: Soil microbiota, particularly fungi, play a central role in plant growth promotion and nutrient cycling. Among them, Aspergillus niger stands out due to its biotechnological potential, contributing to phosphate solubilization and enhancing plant development. However, the application of such microorganisms requires robust strategies to enable precise and reliable tracking of the fate and behavior of introduced strains under environmental conditions. In this context, this study advances the genomic characterization of A. niger by focusing on microsatellites (short tandem repeats, STRs) for the identification of genetic markers with high discriminatory power among strains, through an integrated in silico workflow of detection, variability assessment and validation. In addition to expanding the characterization of these markers, the proposed strategy provides an efficient, reproducible, scalable, and cost-effective computational framework that reduces reliance on experimental procedures during the early stages of molecular marker development. 2026-04-30T00:00:00Z https://repositorio.ufu.br/handle/123456789/49733 Title: Análise do ganho de produtividade de soja inoculada com microrganismos solubilizadores de fósforo por meio de informações geoespaciais Abstract: One of the major challenges of soybean cultivation in tropical regions is the low soil fertility, which limits phosphorus availability and leads to the intensive use of synthetic chemical fertilizers. This increases production costs, creates environmental impacts, and promotes dependence on imported inputs. In modern agriculture, beneficial microorganisms have emerged as alternatives for improving nutrient use efficiency, especially those capable of solubilizing low-solubility inorganic phosphates. This represents a promising, environmentally sustainable, and economically viable approach that contributes to increasing the availability of this nutrient to plants. The interaction between inoculants and plants, in addition to being complex, can manifest subtly. In such cases, classical statistical methods are generally unable to reveal productivity differences between treated areas, particularly when considering the full dynamics involved in the various developmental stages of the crop. In this context, geostatistical tools have been used to estimate spatial relationships between agricultural yield and different crop management parameters. The aim of this study is to evaluate soybean yield gains associated with inoculation using phosphorus-solubilizing microorganisms through statistical and geostatistical analysis of georeferenced yield data and, additionally, to determine whether spectral variables derived from multispectral images can explain and predict this yield using machine-learning algorithms. The results showed that the Random Forest algorithm provided the best soybean yield estimates with the lowest error, supported by multispectral images that incorporated information about the crop’s phenological dynamics, generating accurate estimates with reduced physical effort. With a large amount of training data, the kriging technique made it possible to generate a surface showing an average yield increase of 3.93%, validating the effectiveness of the P-solubilizing agent. 2025-11-28T00:00:00Z https://repositorio.ufu.br/handle/123456789/48962 Title: Discrimination of coffee diseases and pests using multispectral imaging Abstract: A identificação precisa de doenças e pragas em cafeeiros é crucial para o manejo dirigido da lavoura e para evitar perdas de produtividade. Neste estudo, utilizaram-se imagens multiespectrais para monitorar um cafezal irrigado por gotejamento da cultivar Topázio MG 1190, implantado em 2016 na Universidade Federal de Uberlândia – Campus Monte Carmelo, com o objetivo de discriminar sintomas causados por Leucoptera coffeella (bicho mineiro), Cercospora coffeicola (cercosporiose) e Hemileia vastatrix (ferrugem do cafeeiro). As imagens, capturadas mensalmente de fevereiro a novembro de 2024 com um drone DJI Phantom 4, foram analisadas por meio de modelos de classificação supervisionada de aprendizado de máquina (Redes Neurais e Random Forests) desenvolvidos no software Weka. Paralelamente, foram avaliados 4 pares de folhas por planta (terço médio) para analisar a incidência de cada doença/praga. Os dados de entrada para as classificações consistiram nas bandas espectrais originais do sensor, nos índices de vegetação derivados e na classe designada por planta. Posteriormente, a metodologia foi testada em uma lavoura comercial em julho de 2025. Os resultados demonstraram que os modelos de classificação atingiram até 85% de acurácia e índice Kappa de 0,8, evidenciando o potencial do uso de imagens multiespectrais para o monitoramento contínuo das principais doenças e pragas da cafeicultura. Além disso, os mapas gerados pelos modelos permitiram analisar a distribuição espacial dos problemas fitossanitários estudados, mostrando-se uma ferramenta de apoio a práticas de manejo localizado. Desse modo, a pesquisa evidenciou que essa ferramenta pode ser uma alternativa para a promoção da cafeicultura sustentável, contribuindo para o controle mais eficiente de doenças e pragas. 2026-02-23T00:00:00Z