Application of Digital Image Analysis to the Prediction of Chlorophyll Content in <i>Astragalus</i> Seeds

• Main Authors: Yanan Xu, Keling Tu, Ying Cheng, Haonan Hou, Hailu Cao, Xuehui Dong, Qun Sun
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Applied Sciences
• Subjects: <i>Astragalus</i> seeds; chlorophyll content; multiple linear regression (MLR); multilayer perceptron (MLP); R; G
• Online Access: https://www.mdpi.com/2076-3417/11/18/8744

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 R² of the MLR prediction model established with multiple features was ≥0.947, and the R² of the MLP model was ≥0.943. By sorting of two single features, the R and G values, the R² 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.