Abstract: Creation, adjustments and adoption of tests and tools that help in the prediction of seed storability have been highly demanded. Therefore, this work aimed to analyze the efficiency of different artificial aging times in predicting the performance of soybean seeds after storage, using the GGE biplot method. Seeds of six genotypes were subjected to storage, under refrigerated and non-refrigerated conditions, and artificial aging, being artificially aged for periods of 0, 48, 96 and 144 hours. Seeds freshly harvested and after natural and artificial aging were subjected to germination and vigor tests. The experiments were analyzed separately, using means test, regression analysis and model identity test, and together, using the GGE biplot method. Artificial aging at a temperature of 41 °C for 96 hours has the potential to be used to predict the performance of soybean seeds after eight months of storage. The GGE biplot is a method that can be used as a tool to analyze the relationships between aging environments and visualize the ranking of genotypes regarding the performance of seeds subjected to natural and artificial aging.

Resumo: Tem sido altamente requerida a elaboração, ajustes e adoção de testes e ferramentas que auxiliem na predição da armazenabilidade de sementes. Diante disso, este trabalho teve por objetivo analisar a eficiência de diferentes tempos de envelhecimento artificial na predição do desempenho das sementes de soja após o armazenamento, utilizando-se o método GGE biplot. Sementes de seis genótipos foram submetidas ao armazenamento, sob condição refrigerada e não refrigerada, e envelhecimento artificial, sendo envelhecidas artificialmente pelos períodos de 0, 48, 96 e 144 horas. As sementes recém-colhidas e após o envelhecimento natural e artificial foram submetidas a testes de germinação e vigor. Os experimentos foram analisados separadamente, por meio de teste de médias, análise de regressão e teste de identidade de modelos, e em conjunto, utilizando-se o método GGE biplot. O envelhecimento artificial, à temperatura de 41 °C por 96 horas apresenta potencial para ser utilizado na predição do desempenho de sementes de soja após oito meses de armazenamento. O GGE biplot é um método que pode ser utilizado como ferramenta para analisar as relações entre os ambientes de envelhecimento e visualizar o ranqueamento dos genótipos quanto ao desempenho das sementes submetidas ao envelhecimento natural e artificial.

ABSTRACT This work aimed at investigating the viability of near infrared spectrometry (NIR), associated with chemometric methods, in order to identify differences at the levels of vigor of naturally and artificially aged soybean seeds. Seeds of six soybean genotypes were analyzed when freshly harvested, after natural aging in storage for eight months, and after artificial aging at the temperature of 41 °C for 96 hours. The seed moisture content, germination potential and vigor were evaluated. Also, there were measured the content of protein, oil and of the fatty acids: palmitic, stearic, oleic, linoleic and linolenic. The NIR spectra were obtained from the freeze-dried and grinded seeds. The natural and artificial aging of the seeds promote deterioration at distinct levels, reflecting in differences in seed vigor. The regions of the electromagnetic spectrum between wavelengths of 1000-1200 nm, 1350-1450 nm, 1800-1900 nm and 2300-2400 nm are important to distinguish the levels of quality of the soybean seeds. The contents of oil and protein have a relationship with the physiological quality of the seeds. Also, the most relevant wavelengths for the classification of seed vigor present a relationship with these compounds. NIR spectroscopy, in combination with chemometric methods, presents potential to be used in the classification of soybean seeds regarding their physiological quality.

ABSTRACT. The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds.

Abstract: The search for techniques that allow for the rapid and accurate assessment of seed vigor, such as the Seedling Analysis System (SAPL®) and ILASTIK®, can be promising alternatives for seedling image analysis. The objective of this work was to classify the vigor of lentil seeds using seedling image analysis techniques and interactive machine learning. Seeds from seven lots were characterized for physiological potential through germination and vigor tests. For computerized seedling analysis, the seeds were subjected to seedling growth tests at 20 °C for three, four, five, and ten days, and then photographed using a digital camera. The images were processed using SAPL® software, yielding values for total length, root length, shoot length, and vigor, growth, and uniformity indices. ILASTIK® provided data on the percentage of vigorous seedlings, non-vigorous seedlings, and dead seeds. The total length of seedlings, root length, shoot length, and vigor indices determined at 4 days of germination by SAPL® allowed for the classification of lots in terms of vigor. Data obtained by ILASTIK® at 4 days of germination, used in machine learning studies, enable the development of models with high accuracy for seed vigor assessment.

Resumo: A busca por técnicas que possibilitem avaliar o vigor de sementes de forma rápida e assertiva como o Sistema de Análise de Plântulas (SAPL®) e o ILASTIK® podem ser alternativas promissoras para a análise de imagem de plântulas. O objetivo do trabalho foi realizar a classificação do vigor de sementes de lentilha utilizando técnicas de análise de imagem de plântulas e aprendizagem interativa de máquina. Sementes de sete lotes foram caracterizadas quanto ao potencial fisiológico pelos testes de germinação e vigor. Para a análise computadorizada de plântulas, as sementes foram submetidas ao teste de crescimento de plântulas a 20 °C por três, quatro, cinco e dez dias e, em seguida, fotografadas utilizando uma câmera digital. As imagens foram processadas pelo software SAPL®, obtendo-se valores de comprimento total, da raiz, parte aérea e índices de vigor, crescimento e uniformidade. Pelo ILASTIK®, foram obtidos dados de porcentagem de plântulas vigorosas, não vigorosas e sementes mortas. O comprimento total das plântulas, raiz, parte aérea e os índices de vigor determinados aos 4 dias de germinação pelo SAPL® permitem classificar os lotes quanto ao vigor. Os dados obtidos pelo ILASTIK®, aos 4 dias de germinação, utilizados nos estudos de aprendizagem de máquina, permitem o desenvolvimento de modelos com alta precisão para avaliação do vigor das sementes.