I have several packets from Baker Creek that I bought two years ago. Thanks for the idea to test them this way! This site uses Akismet to reduce spam. Learn how your comment data is processed. Sep Sep 9. Sep 1. Aug Aug 9. Aug 7.
Seed Viability and Vigour
Proud to Have Been Featured Here. Our work indicated that these two classifiers could distinguish the viability of wheat seeds. Overall classification accuracy is a weighted average of viability and non-viability accuracy, which is influenced by both the accuracy of determining the viability and non-viability, and by the frequency of viable and non-viable seeds. In this work, the original germination percentage of the seed lot was Therefore, high overall accuracy cannot completely reflect the performance of the models. When selecting a practical model for seed viability classification, not only the overall accuracy, but also the accuracy of viable seeds and the enhancing effect of final germination percentage should be considered as selection criteria.
In this model, high overall accuracy and viable seeds were obtained by only using selected wavelengths. This system greatly reduced the number of used wavelengths from to 16, and could be applied to develop multispectral screening machines for seed companies and farmers. In addition to providing high classification capability, it is cost-effective, requiring only one scanner to acquire spectral information. Acquisition of average spectral data requires two cameras to simultaneously screen with a specific machine that is needed to keep the seeds in the appropriate orientation for reverse-side information acquisition, negatively affecting the speed and cost of production.
The methodology of hyperspectral image acquisition, pre-processing, and of wavelength and classifier selection was established for the classification of viable and non-viable wheat seeds. Likewise, the SPA method, which extracted the most effective wavebands and optimized models, was suitable for wheat seed characterization according to viability.
Furthermore, after screening by this model, the average final germination percentage of the seed lot could exceed Table S1. Table S2. Table S3. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online Mar 8. Find articles by Tingting Zhang. Find articles by Bin Zhao. Find articles by Ranran Wang. Find articles by Mingliu Li. Find articles by Jianhua Wang. Find articles by Qun Sun. Author information Article notes Copyright and License information Disclaimer. Received Jan 15; Accepted Mar 6.
This article has been cited by other articles in PMC. Associated Data Supplementary Materials sensorss Introduction Seeds are the basis of the agricultural industry [ 1 ]. Materials and Methods 2. Seed Preparation Dry seeds of the wheat cultivar Dunmaiwang were purchased from a local market Jinan, China in October Hyperspectral Imaging System An assembled hyperspectral imaging system covering the range of — nm was applied to acquire images of wheat seeds Figure 1. Open in a separate window.
Image Acquisition and Calibration Each wheat seed, both ventral groove and reverse sides, was placed on the translation stage and transmitted to the camera to be scanned line by line at 1. Germination Assessment After HSI spectra collection of samples of both sides of the wheat seeds, a germination test was implemented to check for seed viability [ 37 ]. Spectral Data Extraction After the images were corrected, the background of every image was removed, according to the contrast of the relative reflectance intensity. Spectra Preprocessing Spectral noise e.
Figure 2. Optimal Wavelength Selection The high-dimensional hyperspectral data, comprised of many congruent wavelengths, suffered from multicollinearity and redundancy. Development of Classification Models Choosing a reliable classifier is a significant step in building a classification model. Table 1 Different proportions of the ventral groove side and the reverse side for constructing the calibration set and prediction set.
Results 3. Spectral Characteristics Hyperspectral information from both sides of wheat seeds was acquired Figure 3. Figure 3. Optimal Wavelengths Selected by the SPA Algorithm After preprocessing, the optimal wavelengths were chosen by SPA, which is a novel method to minimize the collinearity among wavebands and select the optimal wavebands. Table 2 Selected wavelengths by successive projections algorithm SPA.
Table 3 The best results of models based on each spectral dataset. Datasets Pre-Processing No. Table 4 Results of the models based on different proportions of the ventral groove side and reverse side. Discussion To select the best pre-processing methods for reducing noise interference in the spectral data and to enhance model accuracy, different preprocessing methods were applied to investigate the model performance. Conclusions The methodology of hyperspectral image acquisition, pre-processing, and of wavelength and classifier selection was established for the classification of viable and non-viable wheat seeds.
Click here for additional data file. Conflicts of Interest The authors declare no conflict of interest. References 1. Huang M. Classification of maize seeds of different years based on hyperspectral imaging and model updating. Dumont J. Thermal and hyperspectral imaging for Norway spruce Picea abies seeds screening.
Soybean seed viability and changes of fatty acids content as affected by seed aging.
How to Test Seed Viability - BBC Gardeners' World Magazine
Dong K. Proteomic analysis reveals key proteins and phosphoproteins upon seed germination of wheat Triticum aestivum L. Plant Sci. Hosomi S. Improved assessment of viability and germination of Cattleya Orchidaceae seeds following storage. In Vitro Cell.
Olesen M. Viability prediction of Ricinus cummunis L. Anisur R. Assessment of seed quality using non-destructive measurement techniques: A review. Seed Sci. Merritt D. A continental-scale study of seed lifespan in experimental storage examining seed, plant, and environmental traits associated with longevity. Downes K. Plant germplasm conservation in Australia: Strategies and guidelines for developing, managing and utilising ex situ collections. Austral Ecol. Shrestha S. Single seed near-infrared hyperspectral imaging in determining tomato Solanum lycopersicum L.
Actuators B Chem. Review of seed quality and safety tests using optical sensing technologies. Nansen C. Using hyperspectral imaging to determine germination of native Australian plant seeds. Tsai M. Machine vision based path planning for a robotic golf club head welding system. Learning techniques used in computer vision for food quality evaluation: A review. Food Eng. Brosnan T. Inspection and grading of agricultural and food products by computer vision systems—A review. Yang X. Grape seed characterization by NIR hyperspectral imaging.
The incubation time varies with seed type and morphology. Remove the seed coats of larger seeds like legume seeds before examination.
How to Test Seeds for Viability
The dicot seeds can be germinated further as the stained seeds are not damaged. When performed appropriately, the percentage of viable seeds obtained by tetrazolium assay is very close to the percentage of seed germination expected under most favourable conditions. Acknowledgments This protocol was adapted from Porter et al. References Porter, R. The use of 2, 3, 5-triphenyl-tetrazoliumchloride as a measure of seed germinability. Plant Physiol 22 2 : Verma, P. Plant Physiol 3 : Wharton, M. The use of tetrazolium test for determining the viability of seeds of the genus Brassica.
Proc Int Seed Test Assoc Bio-protocol 3 17 : e DOI: Test substitute the germination test, explain with reasons? Lee Chen Bioriginal Hi Pooja, My borage seeds have thick and black seed coats that make the staining solution hard to get in contact with the endosperm or other tissues inside the seeds. What do you think can be done to improve the permeability of the staining solution through a thick seed coat?
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