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.

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.

The aim of the present study was to evaluate the effects of temperature, substrate, and luminosity on chia seeds in order to determine adequate conditions for a germination test. The experiment consisted of a completely randomized design, in a 4 × 5 × 2 factorial scheme (four substrates × five temperatures × two luminosity conditions), with four replications. The following variables were analyzed: first germination count, germination, germination speed index, and mean germination time. Germination was best at 15, 20, 25, and 30 °C for the on-paper, paper roll, and sand substrates. However, in the presence of luminosity and at 25 °C, the on-paper substrate had the highest mean. For all substrates, the lowest germination was achieved at 35 °C, proving that high temperatures reduce chia seed germination. Germination speed index was highest at 25 and 30 °C for on-paper, paper roll, and sand substrates, in both the presence and absence of luminosity. Chia seeds are considered to be neutral photoblasts. Germination tests should be performed with the on-paper substrate, at 25 °C, and with a duration of seven days. The first count should be carried out four days after establishment of the test.

Among grain-producing species, soybean is one of the most important commodities, with increasing demand for production in coming years. Evaluation of soybean seed quality is fundamental for ensuring maximum germination and yield potential. Therefore, effective methods are necessary for examining different properties associated with physical-chemical, physiological, and seed-health changes that affect seed quality. This review focuses on the fundamental principles and on the application of techniques of radiographic imaging, magnetic resonance imaging, multispectral imagining, chlorophyll fluorescence imaging, and infrared thermography to evaluate changes related to loss of soybean seed quality, such as mechanical injury, injury caused by insects, embryonic malformation, and incomplete maturation. Computerized seedling image analysis is also presented for evaluation of seed lot vigor. The examples presented here show the potential of these image analysis techniques for identifying different types of injuries and increasing the efficiency of in-house quality control programs in soybean seed production companies.

As sementes de graviola (Annona muricata) apresentam tegumento resistente e impermeável, além de substâncias inibidoras que dificultam a germinação das sementes. Assim, objetivou-se avaliar a contribuição da embebição em ácido giberélico para a superação da dormência e aumento da germinação das sementes de graviola. O delineamento experimental foi inteiramente casualizado, com cinco tratamentos e quatro repetições. As sementes foram escarificadas e em seguida embebidas por 24 horas a 25 ºC em soluções contendo 0; 50; 100; 150 e 200 mg L^-1 (ppm) de ácido giberélico. Após este período de embebição as sementes foram submetidas ao teste de germinação, sendo avaliados a porcentagem de germinação, o índice de velocidade e o tempo médio de germinação. As concentrações de ácido giberélico apresentaram efeito sobre a germinação e índice de velocidade de germinação. Conclui-se que a embebição das sementes de graviola na concentração aproximada de 140 ppm de ácido giberélico auxilia na superação da dormência, aumentando a germinação e o índice de velocidade de germinação.

Crambe is a rapeseed with high oil content and can be used as a winter cover or as a source of raw material for the production of biodiesel, however espite the growing interest in the culture, research on the subject is still incipient, especially concerning the seed production and analysis technology. The purpose of this study is to evaluate the physiological quality of crambe seeds, 'FMS Brilhante' cultivar, by testing the pH of exudate. Five seed lots were submitted to the determination of water content and the tests of germination and vigor (first count, emergence and tetrazolium). In the conduction of pH exudate test, temperatures (25 and 30^oC), and periods of seed imbibition in water (15, 30 and 45 minutes) were tested. The experiment was conducted in a completely randomized manner, with four replicates, and the mean values were compared by the Tukey test at 5% probability; Pearson correlation between the pH of the exudate and initial tests was also made. Testing the pH of exudate is promising for separating lots of crambe seeds and the following combinations of 25°C/30 minutes or 30°C/45 minutes can be used.