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Fabiano França-Silva
USP, Brazil

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Journal article
Published: 01 January 2021 in Journal of Seed Science
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The use of computational resources to assess the physiological potential of seeds has attracted the attention of researchers and professionals in the seed sector. The aim of this study was to evaluate the vigor of cowpea seeds using the Vigor-S software (Automated Seed Vigor Analysis System), in comparison to the information provided by traditional vigor tests that are recommended for the species. Seven seed lots of the cultivar BRS Guariba were used. They underwent germination and seed vigor assessment by testing first germination count, accelerated aging, electrical conductivity, seedling emergence, speed of seedling emergence and use of Vigor-S, an automated image system for seedling assessment, in two evaluation periods. The data underwent analysis of variance, in a completely randomized design and their means were compared by the Scott-Knott test (p ≤ 0.05). An analysis was made of the correlation between the vigor tests traditionally used and the variables provided by Vigor-S. Using the Vigor-S software for automated image analysis of cowpea seedlings three days after sowing is an efficient alternative to detect differences in vigor between seed lots, similarly to traditional seed vigor evaluations.

ACS Style

Carlos Henrique Queiroz Rego; Silvio Moure Cicero; Fabiano França-Silva; Francisco Guilhien Gomes-Junior. Assessing the vigor of cowpea seeds using the Vigor-S software. Journal of Seed Science 2021, 43, 1 .

AMA Style

Carlos Henrique Queiroz Rego, Silvio Moure Cicero, Fabiano França-Silva, Francisco Guilhien Gomes-Junior. Assessing the vigor of cowpea seeds using the Vigor-S software. Journal of Seed Science. 2021; 43 ():1.

Chicago/Turabian Style

Carlos Henrique Queiroz Rego; Silvio Moure Cicero; Fabiano França-Silva; Francisco Guilhien Gomes-Junior. 2021. "Assessing the vigor of cowpea seeds using the Vigor-S software." Journal of Seed Science 43, no. : 1.

Journal article
Published: 17 August 2020 in Agriculture
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Recent advances in multispectral imaging-based technology have provided useful information on seed health in order to optimize the quality control process. In this study, we verified the efficiency of multispectral imaging (MSI) combined with statistical models to assess the cowpea seed health and differentiate seeds carrying different fungal species. Seeds were artificially inoculated with Fusarium pallidoroseum, Rhizoctonia solani and Aspergillus sp. Multispectral images were acquired at 19 wavelengths (365 to 970 nm) from inoculated seeds and freeze-killed ‘incubated’ seeds. Statistical models based on linear discriminant analysis (LDA) were developed using reflectance, color and texture features of the seed images. Results demonstrated that the LDA-based models were efficient in detecting and identifying different species of fungi in cowpea seeds. The model showed above 92% accuracy before incubation and 99% after incubation, indicating that the MSI technique in combination with statistical models can be a useful tool for evaluating the health status of cowpea seeds. Our findings can be a guide for the development of in-depth studies with more cultivars and fungal species, isolated and in association, for the successful application of MSI in the routine health inspection of cowpea seeds and other important legumes.

ACS Style

Carlos Rego; Fabiano França-Silva; Francisco Gomes-Junior; Maria Moraes; André Medeiros; Clíssia Silva. Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea. Agriculture 2020, 10, 361 .

AMA Style

Carlos Rego, Fabiano França-Silva, Francisco Gomes-Junior, Maria Moraes, André Medeiros, Clíssia Silva. Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea. Agriculture. 2020; 10 (8):361.

Chicago/Turabian Style

Carlos Rego; Fabiano França-Silva; Francisco Gomes-Junior; Maria Moraes; André Medeiros; Clíssia Silva. 2020. "Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea." Agriculture 10, no. 8: 361.

Journal article
Published: 12 June 2020 in Sensors
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Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.

ACS Style

Fabiano França-Silva; Carlos Henrique Queiroz Rego; Francisco Guilhien Gomes-Junior; Maria Heloisa Duarte De Moraes; André Dantas De Medeiros; Clíssia Barboza Da Silva. Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging. Sensors 2020, 20, 1 .

AMA Style

Fabiano França-Silva, Carlos Henrique Queiroz Rego, Francisco Guilhien Gomes-Junior, Maria Heloisa Duarte De Moraes, André Dantas De Medeiros, Clíssia Barboza Da Silva. Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging. Sensors. 2020; 20 (12):1.

Chicago/Turabian Style

Fabiano França-Silva; Carlos Henrique Queiroz Rego; Francisco Guilhien Gomes-Junior; Maria Heloisa Duarte De Moraes; André Dantas De Medeiros; Clíssia Barboza Da Silva. 2020. "Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging." Sensors 20, no. 12: 1.

Article
Published: 11 June 2020 in Agronomy Journal
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Radiographic and multispectral image analysis have potential to be efficient, objective methods for assessing seed quality and internal insect infestation. The aim of this study was to verify the efficiency of radiographic and multispectral analysis in detecting signs and damage caused by Angoumois grain moth [Sitotroga cerealella (Olivier)] and its different developmental stages in wheat (Triticum aestivum L.) seeds. The experiment was conducted in a completely randomized design with six replications of 50 seeds. The samples were subjected to laboratory‐induced infestation and after 5 and 10 d, radiographic and multispectral analysis were conducted. Afterwards, the seeds were immersed in water for 24 h and then sectioned with a cutting blade. The number of seeds with signs of eggs or oviposition, larvae, pupae, adult insects and insect galleries was quantified. The generalized linear models (GLM) methodology was used and the Tukey test (p < .05) was used to compare the means. In general, the radiographic (with or without contrast) and multispectral methods are viable tools to evaluate insect‐infested and uninfested wheat seeds. Multispectral analysis was efficient only in identifying eggs on the seed surface and does not detect the presence of larvae and pupae inside the seeds.

ACS Style

Fabiano França‐Silva; Carlos Henrique Queiroz Rego; Francisco Guilhien Gomes‐Junior; Vivian Aparecida Brancaglioni; Welinton Yoshio Hirai; Daniele Brandstetter Rodrigues; Andreia Da Silva Almeida; Andrea Bicca Noguez Martins; Lilian Vanussa Madruga De Tunes. Determination of Sitotroga cerealella infestation in wheat seeds by radiographic and multispectral images. Agronomy Journal 2020, 112, 3695 -3703.

AMA Style

Fabiano França‐Silva, Carlos Henrique Queiroz Rego, Francisco Guilhien Gomes‐Junior, Vivian Aparecida Brancaglioni, Welinton Yoshio Hirai, Daniele Brandstetter Rodrigues, Andreia Da Silva Almeida, Andrea Bicca Noguez Martins, Lilian Vanussa Madruga De Tunes. Determination of Sitotroga cerealella infestation in wheat seeds by radiographic and multispectral images. Agronomy Journal. 2020; 112 (5):3695-3703.

Chicago/Turabian Style

Fabiano França‐Silva; Carlos Henrique Queiroz Rego; Francisco Guilhien Gomes‐Junior; Vivian Aparecida Brancaglioni; Welinton Yoshio Hirai; Daniele Brandstetter Rodrigues; Andreia Da Silva Almeida; Andrea Bicca Noguez Martins; Lilian Vanussa Madruga De Tunes. 2020. "Determination of Sitotroga cerealella infestation in wheat seeds by radiographic and multispectral images." Agronomy Journal 112, no. 5: 3695-3703.

Journal article
Published: 01 June 2019 in Insects
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Technologies that increase safety and efficiency, while facilitating and streamlining the work of seed analysts, are increasingly required by the seed industry. X-ray image analysis is a technique that has been used in the analysis of grain and seeds because it is fast, accurate and non-destructive. The traditional method to verify the presence of insect damage in seeds involves manual cutting of the seeds, which endangers the safety of the analyst and is time-consuming and repetitive work that leads to visual fatigue. The objective of this study was to compared the efficiency of radiographic analysis with and without contrast in the determination of infestation by Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae), at different stages of development, in maize seeds, compared to the traditional method required by seed legislation, which consists of cutting and visual evaluation. Seeds were evaluated regarding the presence of eggs/oviposition signs, larvae, pupae, adult insects, insect damage in five infestation periods (5, 18, 33 and 35 days after infestation), while evaluating the total number of seeds infested, comparing the three methods. For characterization of the oviposition stage, the use of contrast was best at all times of infestation. For the larval stage, there was no difference between the evaluation methods; however, at 18 days, larger infestations were observed by the traditional method. At 5 days, the identification of pupae was better by the traditional method and radiography without contrast, while for the identification of adult insects the best method was the use of radiography without contrast. The characterization of the level of infestation with maize weevil damage was best verified using contrast radiography. Radiographic analysis is efficient in the detection of damage caused by S. zeamais in maize seeds. This method of radiographic analysis (with or without contrast) is thus an auxiliary tool to assess the damage and presence of S. zeamais in maize seeds.

ACS Style

Maria Laene Moreira De Carvalho; Eva Rezende Leite; Geraldo Andrade Carvalho; Fabiano França-Silva; Dayliane Bernardes De Andrade; Elizabeth Rosemeire Marques. The Compared Efficiency of the Traditional Method, Radiography without Contrast and Radiography with Contrast in the Determination of Infestation by Weevil (Sitophilus zeamais) in Maize Seeds. Insects 2019, 10, 156 .

AMA Style

Maria Laene Moreira De Carvalho, Eva Rezende Leite, Geraldo Andrade Carvalho, Fabiano França-Silva, Dayliane Bernardes De Andrade, Elizabeth Rosemeire Marques. The Compared Efficiency of the Traditional Method, Radiography without Contrast and Radiography with Contrast in the Determination of Infestation by Weevil (Sitophilus zeamais) in Maize Seeds. Insects. 2019; 10 (6):156.

Chicago/Turabian Style

Maria Laene Moreira De Carvalho; Eva Rezende Leite; Geraldo Andrade Carvalho; Fabiano França-Silva; Dayliane Bernardes De Andrade; Elizabeth Rosemeire Marques. 2019. "The Compared Efficiency of the Traditional Method, Radiography without Contrast and Radiography with Contrast in the Determination of Infestation by Weevil (Sitophilus zeamais) in Maize Seeds." Insects 10, no. 6: 156.