New Technologies for Germination Testing

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1 New Technologies for Germination Testing Some facts, ideas and examples for inspiration Bert van Duijn

2 New tools in germination evaluation Evaluation of non-germinated (dry)seeds( Prediction of germination Prediction of plantlet quality Evaluation of germinated seeds Automation of standard germination test or field test Counting Plantlet evaluation

3 Evaluation of non-germinated (dry)seeds The germination quality of seeds is determined by: Physiological factors Genetic factors Structural factors Biological factors (e.g. microbiology) Hence, it is very unlikely that germination quality can be determined with one method/measurement/parameter

4 Measurement of material (chemical)) and structural properties on the surface (or just below) Spectroscopy Image analysis Hyperspectral analysis and imaging

5 Spectral (image) analysis Visible light X-ray UV (Near) infra red Specific wavelengths NMR Acoustics Reflection image Fluoresence image Absorbance image Other ATC, ISTA 5

6 SURFACE PROPERTIES & LEAKING PROPERTIES Classifying deteriorated seeds (Brassicaceae) by sinapine leakage (T.G. Min, Daegu Univeristy, South-Korea) Fluorescence from sinapine leakage under UV light in the dark room

7 C C NF F F NF C: control seeds, NF: non-fluorescent seeds, F: fluorescent seeds (T.G. Min, Daegu Univeristy, South-Korea)

8 Identifying deteriorated seeds by NIR Schematic diagram for measuring NIR spectra of the intact single seed. (T.G. Min, Daegu Univeristy, South-Korea)

9 Principle component score plots for radish seeds. (+: viable seed, : nonviable seed) NIR spectra of radish seeds. (a: raw spectra, b: mean spectra of raw, c: first derivative of the mean spectra) (T.G. Min, Daegu Univeristy, South-Korea)

10 Prediction accuracy of viable and nonviable radish seeds classified by PLS 2 models from raw, 1st, and 2nd derivative of NIR spectra data sets. (T.G. Min, Daegu Univeristy, South-Korea)

11 * Significant at P=0.05, **Significant at P=0.01, NS=non significance Chemical elements on the surfaces of seed coat and cotyledon compared to viable and nonviable seeds.

12 Nondestructive Separation of Viable and Non-viable Gourd (Lagenaria siceraria Standl) Seeds NIR spectra of gourd seeds. (A: original, B: mean spectra of original, C: first derivative of the mean spectra) Principle component score plot for gourd seeds (+: viable seed, : nonviable seed.) (T.G. Min, Daegu Univeristy, South-Korea)

13 Micrographs of viable seed coat (A) and nonviable seed coat (B) in the B spot (x 500). Bar = 100 µm. (T.G. Min, Daegu Univeristy, South-Korea)

14 Micrograph of viable cotyledon (A) and nonviable cotyledon (B) in the B spot (x 2,000). Numerous bacteria were contaminated in a nonviable cotyledon (B) surface while not in a viable one (A). Bar = 20 µm. (T.G. Min, Daegu Univeristy, South-Korea)

15 Sample No. Viable seed Non-viable seed A y B C A B C 1 0 x Aveage Bacteria number at three spots on the surfaces of gourd cotyledon under the Field Emission Scanning Electron Microscope. (T.G. Min, Daegu Univeristy, South-Korea)

16 chlorophyll fluorescence of germinating seeds (Data from H. Jalink, WUR, Netherlands)

17 Hyperspectral cameras and images Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. Much as the human eye sees visible light in three bands (red, green, and blue), spectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible.

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21 Measurement of internal structures X-ray based imaging 3-D (x-ray) imaging

22 Relationship Röntgen-seed-images and plant-quality of cucumber Abnormal seeds Abnormal plants Good seed Good plant

23 High Resolution X-ray X image of Watermelon Seeds (3n) Raw Enhanced 1 Enhanced 2 23

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25 Germination measurement plant counting in the lab in the field Plantlet imaging Plantlet markers

26 Chlorophyll fluorescence

27 Labeling of plantlets via uptake of marker via seed

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30 Conclusions No single technique/parameter able to predict germination quality Multi (hyper) spectral imaging is promising in combination with other technologies) New imaging technology may help to automate germination test analysis (in the lab and in the field)