Shade Increased Seed Yield and Quality of Incarvillea sinensis var. przewalskii.

Incarvillea sinensis var. przewalskii has attracted great attention because of the anticancer value of its alkaloids and the potential use of the species for ecological restoration. However, the scarcity of high-quality seeds has significantly hindered the cultivation and efficient utilization of this species. Understanding how seeds respond to maternal environmental conditions is crucial for developing high-yield and top-notch seed accessions, but the available knowledge in this area is limited. Here, we determined the effect of shading treatments on seed development, seed quality, and yield. Compared to the control, shade significantly increased the seed germination rate and 1000-seed weight by 29.2% and 25.6%, respectively. Regardless of light conditions, the seed germination rate and 1000-seed weight decreased by 7.13% and 37.5%, respectively, as the fruit positioned from base to apical. The seed yield per plant was 27.9% higher under shade than under the control treatment. The structural equation model showed that shade promoted seed yield through increasing flowers per reproductive branch and seed numbers per capsule. These findings suggest that adjusting shading conditions and optimizing inflorescence development can lead to high-yield and high-quality seeds. Additionally, prioritizing the number of flowers per reproductive branch and seeds per capsule in breeding programs can further enhance the seed yield of I. sinensis var. przewalskii.

Cultivars identification of oat (Avena sativa L.) seed via multispectral imaging analysis.

Cultivar identification plays an important role in ensuring the quality of oat production and the interests of producers. However, the traditional methods for discrimination of oat cultivars are generally destructive, time-consuming and complex. In this study, the feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis. The principal component analysis (PCA), linear discrimination analysis (LDA) and support vector machines (SVM) were applied to classify seeds of 16 oat cultivars according to their morphological features, spectral traits or a combination thereof. The results demonstrate that clear differences among cultivars of oat seeds could be easily visualized using the multispectral imaging technique and an excellent discrimination could be achieved by combining data of the morphological and spectral features. The average classification accuracy of the testing sets was 89.69% for LDA, and 92.71% for SVM model. Therefore, the potential of a new method for rapid and nondestructive identification of oat cultivars was provided by multispectral imaging combined with multivariate analysis.