| Summary: | Chlorophyll fluorescence (CF) has been applied to measure the chlorophyll content of seeds, in order to determine seed maturity, but the high price of equipment limits its wider application. <i>Astragalus</i> seeds were used to explore the applicability of digital image analysis technology to the prediction of seed chlorophyll content and to supply a low cost and alternative method. Our research comprised scanning and extracting the characteristic features of <i>Astragalus</i> seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in <i>Astragalus</i> seeds based on characteristic features. The results showed that the R<sup>2</sup> of the MLR prediction model established with multiple features was ≥0.947, and the R<sup>2</sup> of the MLP model was ≥0.943. By sorting of two single features, the R and G values, the R<sup>2</sup> reached 0.969 and 0.965, respectively. A germination result showed that the lower the chlorophyll content, the higher the quality of the seeds. Therefore, we draw a conclusion that digital image analysis technology can be used to predict effectively the chlorophyll content of <i>Astragalus</i> seeds, and provide a reference for the selection of mature and viable <i>Astragalus</i> seeds.
|
|---|