Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds

Abstract

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. Astragalus 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 Astragalus seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in Astragalus 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 Astragalus seeds, and provide a reference for the selection of mature and viable Astragalus seeds.

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

“…Jia et al [13] found that a new combination of spectral indices aided in determining sensitive bands, enhanced the correlation of leaf nitrogen content, and performed reliably in the regression model of nitrogen content in flue-cured tobacco leaves. With the rapid development of machine learning algorithms, multiple linear regression (MLR) [14], partial least squares regression (PLSR) [15], random forest regression (RFR) [16], support vector machine regression (SVR) [17], and other methods have achieved good results in estimating leaf chlorophyll content. Liu et al [18] utilized continuous wavelet transform to analyze the chlorophyll content in potatoes.…”

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

“…The inputs and outputs can be connected by multilayer weighting, with strong self-learning, adaptive, associative memory and parallel processing of things and environments. ( Xu et al., 2021 )…”

“…The inputs and outputs can be connected by multilayer weighting, with strong self-learning, adaptive, associative memory and parallel processing of things and environments. (Xu et al, 2021) To avoid the effect of default parameters on the prediction accuracy of the classification model, the internal parameters of the classification model must be separately adjusted. In the SVM algorithm, the RBF kernel was selected, and it carried out the 5fold internal cross-validation and grid search method to calculate optimal penalty coefficient c and the kernel parameter g. The searching range was both set to -10 to 10 with the step of 0.2 (a total of 101*101 combinations were used to search the best parameters).…”