Statistical features analysis and discrimination of maize seeds utilizing machine vision approach

Willow (Salix babylonica) Extracts Can Act as Biostimulants for Enhancing Salinity Tolerance of Maize Grown in Soilless Culture

Salinity negatively affects agricultural production by reducing crop growth and yield. Botanical biostimulants can be used as innovative and sustainable tools to cope with abiotic stress. In this study, salicylic acid (SA) (25 µM) and willow leaf (WL) (0.1 and 0.2%) and bark (WB) (0.1 and 0.2%) extracts were applied as plant-based biostimulants to hydroponically grown maize in the absence and presence of salinity stress (60 mM NaCl). The hormone-like activity and mineral composition of willow extracts were analyzed, and the effects of willow extracts on growth parameters, chlorophyll content, antioxidative enzyme activities, protein levels and mineral nutrient concentrations of maize plants were measured. Within the tested biostimulant applications, 0.2% WB, 0.1% WL and 0.2% WL gave the most promising results, considering the stress alleviating effects. The shoot biomass was increased up to 50% with 0.1% WL treatment and Na⁺ uptake was reduced with biostimulant applications under saline conditions. Under stress, the protein concentrations of maize leaves were enhanced by 50% and 80% with high doses of WB and WL applications, respectively. Results indicate that willow tree prunings can be valuable bio-economy resources, and aqueous extracts prepared from their leaves and barks can be used as effective and eco-friendly biostimulants.

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000-2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.