Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing ...decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.
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.
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of ...this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.
Successive cycles of water absorption and loss favor weathering deterioration, one of the main factors that affect the quality of soybean seeds. This study evaluated the physiological, physical, and ...morpho-anatomical changes in soybean seeds under weathering deterioration at the pre-harvest phase. Six soybean cultivars (BMX Apolo, DM 6563, NS 5959, NA 5909, BMX Potência, and TMG 1175) were produced in a greenhouse and underwent weathering deterioration through a rainfall simulation system, applying 0, 60, 120, and 180 mm of precipitation at pre-harvest phase. Each rainfall level was divided into two applications at an interval of 72 h: 60 mm (30 + 30), 120 mm (60 + 60), and 180 mm (90 + 90). After harvest, the seeds were evaluated for germination, vigor, physical and morpho-anatomical properties. Weathering deterioration induced by simulated rainfall at the pre-harvest phase contributes to the reduction in soybean seed germination and vigor and is conditioned by the soybean genotype. The increase in intensity of simulated rainfall led to a more significant weathering damage in seeds, as evidenced by the X-ray and tetrazolium test. Cultivars DM 6563 and BMX Potência were more susceptible, while NA 5909 was less susceptible to weathering deterioration (especially at the highest level; 120 mm and 180 mm). Anatomical changes caused by weathering deterioration lead to cell compaction and rupture, mainly in the cell layers of the hourglass and parenchyma, forming intracellular spaces. The presence of weathering damage caused a reduction in physiological soybean seed quality.
A germinação de sementes é dependente de fatores abióticos, sendo a temperatura um dos principais, cuja influência, em condições extremas, causa danos às sementes. Este trabalho teve por objetivo ...investigar o efeito das diferentes temperaturas durante a germinação de Dalbergia nigra e suas implicações na fisiologia das sementes. Avaliaram-se o percentual de germinação, o índice de velocidade de germinação (IVG) e a integridade de membranas celulares pelo teste de condutividade elétrica de sementes em diferentes tempos de exposição às temperaturas de 5, 15, 25 (controle), 35 e 45 ºC. A temperatura de 25 ºC correspondeu à temperatura ideal de germinação. Em temperaturas de 5 e 45 ºC, a germinação foi nula. Houve redução da germinação de sementes de D. nigra com o aumento do tempo de exposição das sementes às temperaturas de 5, 15, 35 e 45 ºC. Diferentemente das demais temperaturas, a semipermeabilidade da membrana não é recuperada nas temperaturas de 5 e 45 ºC. A condutividade elétrica é uma técnica eficiente para avaliar a qualidade fisiológica das sementes em diferentes temperaturas.
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•New free and easy-to-use tool for analysis of seed radiography of agricultural species.•Tissue integrity and seed morphometry can be assessed easily, rapidly, and accurately.•Seed ...viability and vigor can be indirectly assessed through automated analysis of seed radiographs.
Optical technologies that are able to analyze physical properties of biological samples are increasingly drawing interest in modern agriculture. The use of X-rays for analysis of internal properties of agricultural products, such as seeds, has proven its worth in providing information regarding their quality in a non-destructive manner. However, visual evaluations of radiographic images are time-consuming, subjective, and highly prone to error. Therefore, it is necessary to develop methods that allow these analyses to be performed in an efficient and assertive manner. To that end, a free-access, open-source, and easy-to-use tool called IJCropSeed has been developed for high-throughput analysis of radiographic images of seeds from several agricultural crops. In addition, an experiment was conducted in which machine learning models were developed from the information obtained from the tool to predict the seed germination capacity and seedling vigor of Crambeabyssinica. The results showed that IJCropSeed had a high performance for the analysis of digital radiographic images of the 24 agricultural crops evaluated, with high speed and high precision of segmentation of the images. The use of parameters obtained with the tool, in combination with the machine learning models, proved to be highly efficient in classifying the quality of C. abyssinica seeds. It is a non-destructive and highly effective method.
The need to optimize seed quality assessment using new, more accessible, and modern computational resources has led to the emergence of new tools. In this paper, we introduce SeedCalc, a new R ...software package developed to process germination and seedling length data. The functions included in SeedCalc allow fast and efficient data processing, offering greater reliability to the variables generated and facilitating statistical analysis itself since the data are already processed with the appropriate structure to be statistically analyzed in the R software. SeedCalc is available free of charge at https://CRAN.R-project.org/package=SeedCalc.
Resumo: A necessidade de otimizar a avaliação da qualidade de sementes utilizando novos recursos computacionais mais acessíveis e modernos levou ao surgimento de novas ferramentas. Neste artigo, introduzimos o SeedCalc, um novo pacote do software R desenvolvido para processar dados de germinação e comprimento de plântulas. As funções incluídas no SeedCalc permitem um processamento de dados rápido e eficiente, oferecendo maior confiabilidade às variáveis geradas e facilitando a própria análise estatística, uma vez que os dados já são processados com a estrutura apropriada para serem analisados estatisticamente no software R. O SeedCalc está disponível gratuitamente em https://CRAN.R-project.org/package=SeedCalc.
Weathering deterioration affects seed quality, especially in areas with excessive rainfall. This study aimed to evaluate the oxidative stress, physiological quality, and protein metabolism of seeds ...of different soybean cultivars under weathering deterioration at the pre-harvest phase. Six soybean cultivars (BMX Apolo, DM 6563, NS 5959, NA 5909, BMX Potência, and TMG 1175) were subjected to simulated rainfall at the R8 stage. Each level was divided into two applications at 72-h intervals: 60 mm (30 + 30), 120 mm (60 + 60), and 180 mm (90 + 90). Then, the seeds were harvested and evaluated for physiological potential, antioxidative enzymes, hydrogen peroxide, malondialdehyde, proteins, and protease activity. The simulated rainfall allowed the variation in seed moisture, promoting a significant reduction in germination and seed vigor, especially at 120 and 180 mm levels. There were also reductions in antioxidative enzyme activity with weathering deterioration (mainly for catalase, ascorbate peroxidase, and peroxidase), accumulation of hydrogen peroxide and malondialdehyde, and reductions in protein content and protease activity. The proposed rainfall system is efficient in inducing weathering deterioration during the pre-harvest phase and its deleterious effects. Weathering deterioration in soybean seeds in the pre-harvest stage is directly influenced by genotype.
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.
Technologies based on electromagnetic radiation, such as the X-ray technique, has contributed to the establishment of new and promising methodologies for evaluating seed quality. This study aimed to ...relate parameters based on semi-automated analysis of radiographs of crambe seeds to their physiological quality. Radiographic images of seeds from 10 seed lots of cultivar FMS Brilhante were semi-automatically analyzed using ImageJ® software. Measurements of morphometric characteristics and tissue integrity were obtained for the seeds, as well as individually for the seed embryo. Following X-ray test, the seeds were subject to germination and seedling growth test. It was possible to visualize the internal structures of the seeds in the radiographs. There were differences in the physical parameters obtained by the semi-automated analysis of the radiographs between the seed lots. Also, the lots differed regarding the physiological quality of the seeds. Morphometric characteristics and tissue integrity, especially for the seed embryo, showed high correlation with the seed physiological quality. Therefore, this work presents an efficient approach to rapid and non-destructively assess the quality of crambe seeds.
Resumo: Tecnologias baseadas em radiação eletromagnética, como a técnica de raios X, contribuíram para estabelecer novas e promissoras metodologias para avaliar a qualidade de sementes. Nesse sentido, este estudo teve como objetivo relacionar parâmetros baseados na análise semiautomatizada de radiografias de sementes de crambe à sua qualidade fisiológica. Imagens radiográficas de sementes pertencentes a 10 lotes da cultivar FMS Brilhante foram analisadas de forma semiautomatizada por meio do software ImageJ®. Foram obtidas medidas de características morfométricas e integridade tecidual das sementes, bem como individualmente para o embrião das sementes. Após o teste de raio-X, as sementes foram submetidas ao teste de germinação e crescimento de plântulas. Foi possível visualizar as estruturas internas das sementes nas radiografias. Houve diferenças nos parâmetros físicos obtidos pela análise semiautomática das radiografias entre os lotes de sementes. Além disso, os lotes diferiram quanto à qualidade fisiológica das sementes. Características morfométricas e de integridade tecidual, especialmente para o embrião, apresentaram alta correlação com a qualidade fisiológica. Assim, este trabalho apresenta uma abordagem eficiente para avaliar de forma rápida e não destrutiva a qualidade de sementes de crambe.
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