Digital imaging information technology applied to seed germination testing. A review

The application of digital imaging information technology to seed germination testing is discussed. This technology is reviewed in light of recent interest on the development and adoption of sustainable agrosystems joined with a modern strategy of “precision agriculture”, which provides new complex information tools for better crop production. Basic concepts on the patterns of image analysis descriptors of imbibing seed performance are described with the objective of demonstrating the potential of this technique to be adequate for overcoming problems encountered with a standard seed germination test. The application of different image analysis system prototypes in monitoring seed germination of Brassica, as well as several other crop species, has provided encouraging results, highlighting the reliability of this technique to quickly acquire digital images and to extract numeric descriptors of germination and radicle growth events. Another aspect of digital imaging is the possibility to determine the colour space of a two-dimensional seed surface. Experiments carried out on lentil seed germination have shown that quantitative changes in Red-Green-Blue (RGB) colour component density may be considered as markers of the start of germination. In addition, the extracted RGB data may be used to trace a virtual three-dimensional surface plot allowing a better analysis of colour distribution on the lentil’s surface. RGB colour density can also be used to determine any variation in colour due to the ‘browning effect’ as a result of advancing seed deterioration. The potential of RGB markers in classifying sub-samples and maintaining high germination quality in aged seed samples represents a non-destructive method in seed testing and sorting. As a conclusion, the information flow deriving from digital image processing should be integrated with other bio-morphological, taxonomic and ‘omic-system’ databases. The final target should be an interrelated and complex database for a deeper functional and structural knowledge of plant species, which can respond to the needs of farmers, seed industries, biodiversity conservation and seed basic research.