Spinach seeds are sensitive to high temperatures during germination and plant growth. It is known that seed priming with water or with seaweed extract (SWE) may enhance germination and seedling growth. The objective of this study was to evaluate the effects of seed priming with SWE (Ascophyllum nodosum) on the germination, seedling growth and antioxidant capacity of spinach seedlings under conditions of heat stress. Five concentrations of SWE (0.0, 0.15, 0.30, 0.60 and 1.2%) were used for priming the seeds under two temperatures (15 and 30 °C). Seeds primed with 0.3% SWE had the greatest activity of antioxidant metabolism and showed improved germination percentage, germination speed and seedling vigor under conditions of heat stress (30 °C) compared to nonprimed controls. Hydropriming and priming with 0.3% SWE resulted in lower contents of hydrogen peroxide (H2O2) and malondialdehyde (MDA) under heat stress than control (nonprimed spinach seeds). This study showed that seed priming with SWE was effective to mitigate stress due to high temperatures and to improve spinach seed germination and seedling vigor.

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.

Computerized systems for image analysis are alternatives to decrease the subjectivity and speed of assessment of seed physiological potential. The aim of this study was to determine the efficiency of the Seed Vigor Automated Analysis System (Vigor-S) to identify differences in vigor among soybean seed lots compared to results of the Seed Vigor Imaging System - SVIS® and tests recommended for evaluation of soybean seed vigor. Two cultivars were used, BMX Potência RR and 7166 RSF IPRO, each one represented by ten seed lots with similar germination and vigor differences. The seeds were evaluated regarding germination, vigor (tetrazolium, accelerated aging), seedling emergence in the field and SVIS® and Vigor-S analysis, in three experimental periods. Analysis of variance was used on the data in a completely randomized experimental design (laboratory tests) and in randomized blocks (field tests), and the mean values were compared by the Tukey test (p ≤ 0.05). Separation of the seed lots by Vigor-S was consistent with the results obtained in tests recognized as effective in evaluating the physiological potential of soybean seeds, and there was no interference of the different cultivars on the response pattern for vigor. Analysis through Vigor-S proved to be effective in determination of physiological potential and for composition of quality control programs established by soybean seeds production companies.

Automated image analysis of seedlings for vigor evaluation of common bean seeds

The software Seed Vigor Imaging System (SVIS®), has been successfully used to evaluate seed physiological potential by automated analyses of scanned seedlings. In this research, the efficiency of this system was compared to other tests accepted for assessing cucumber (Cucumis sativus L.) seed vigor of distinct seed lots of Supremo and Safira cultivars. Seeds were subjected to germination, traditional and saturated salt accelerated aging, seedling emergence, seedling length and SVIS analyses (determination of vigor indices and seedling growth uniformity, lengths of primary root, hypocotyl and whole seedlings). It was also determined whether the definition of seedling growth/uniformity ratios affects the sensitivity of the SVIS®. Results showed that analyses SVIS have provided consistent identification of seed lots performance, and have produced information comparable to those from recommended seed vigor tests, thus demonstrating a suitable sensitivity for a rapid and objective evaluation of physiological potential of cucumber seeds. Analyses of four-days-old cucumber seedlings using the SVIS® are more accurate and growth/uniformity does not affect the precision of results.