With the aim of studying the relationship between the physiological quality of soybean seeds submitted to three harvest times and seedling emergence in the field, seeds of cultivars UFV-16, Splendor, Vencedora, Confiança, UFV-18, UFV-TN 105, Garantia and Celeste were harvested at stages R8, R8+15 and R8+30 days. The following tests were performed: accelerated aging, electrical conductivity, emergence in sand, emergence speed index and seedling emergence in the field. Germination and vigor were reduced in seeds harvested at 30 days after stage R8 in all the tests, except for the accelerated aging test, which showed a decline in vigor seeds harvested with a delay of 15 days. Seeds of cultivars UFV-16, Confiança and Garantia showed lower physiological quality in most tests, while the seeds of cultivar Celeste had higher physiological quality than the others. There was similarity between the test results of seed quality and seedling emergence in the field.

Physalis peruviana is being incorporated into small fruit’s scene in Brazil. It can be found in local markets, mainly in the cities of São Paulo and Rio de Janeiro, but has still been imported, since Brazilian production is small (Rodrigues et al., 2009). Typical symptoms of orthotospovirus, including chlorotic spots and concentric rings on the leaves were observed on P. peruviana (Fig 1) in two different crop seasons, March-October 2016 and March-October 2017, in an experimental area located in the campus of the University of São Paulo (USP), Piracicaba, state of São Paulo. Each experimental area was composed of 126 plants. For both years, leaf extracts from 30 randomly chosen field plants were tested in plate-trapped antigen - enzyme linked immunosorbent assay (PTA-ELISA), using specific antiserum against the nucleocapsid protein of Groundnut ringspot virus (GRSV). The antiserum was kindly provided by Dr. Alice K. Inoue-Nagata, Embrapa, CNPH, DF, Brazil. Serological results showed an increase of virus i...

This study aimed at evaluating seed health quality, as well as seedling emergence in the field, of eight soybean cultivars (UFV-16, Splendor, Vencedora, Confiança, UFV-18, UFV-TN 105, Garantia, and Celeste) subjected to different harvest periods. To this, seeds were harvested at the growth stages R8, R8+15 days, and R8+30 days, and then submitted to health test and to the test of seedling emergence in field. Results showed that although seeds harvested at 15 and 30 days after the stage R8 had shown a significant increase of fungi infection, especially by Fusarium spp., Phomopsis spp., and Epicoccum spp., the percent seedling emergence in the field was reduced only to seeds harvested 30 days after the reproductive stage R8. Cultivars have shown differentiated behavior between each other in relation to the percent incidence rate of fungi and seedling emergence in the field; and the seeds least infected by fungi were those allowing the highest percent emergence of seedlings in the field.

Licania tomentosa is a widely distributed species in Brazil, commonly used in urban landscaping and environmental restoration. Despite its potential, understanding the relationship between fruit maturation and seedling quality remains limited. This study aimed to evaluate the relationship between maturation stages - classified by epicarp coloration - and seedling performance through RGB colorimetric analysis, fruit morphometry, and the application of machine learning algorithms. Fruits were collected from mother trees and classified into four color stages based on the Munsell color chart. Digital images were analyzed to extract RGB values and morphometric parameters of the fruits using ImageJ® software. Subsequently, seedling emergence, biometric attributes, biomass accumulation, and the Dickson Quality Index (DQI) were evaluated. Yellow-Red fruits produced seedlings with higher emergence rates, greater shoot and root biomass accumulation, and higher DQI values, indicating greater seedling vigor. In contrast, Greenish Green-Yellow fruits resulted in less vigorous seedlings. The Red band was the main indicator of changes in the fruits. Morphometric parameters alone were insufficient to discriminate the maturation stages. Linear Discriminant Analysis correctly classified 90.48 % of the fruits according to their maturation stage. The integration of colorimetric data with machine learning proved to be an effective, non-destructive, and low-cost approach for optimizing seed selection. To enhance the predictive accuracy of the model it is recommended to expand the dataset under natural conditions and explore alternative color systems and complementary fruit traits.