In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).

The aim of this work was to enrich the knowledge on the potential applications of Elaeagnus mollis leaf extracts. For this purpose, the bioactive compounds (phenolic, flavonoid, alkaloid, proanthocyanidin, chlorophyll and carotene content), antioxidant activity, anti-HepG2 cell proliferation, and cholinesterase inhibitory potential (AChE and BChE) of E. mollis leaves which obtained from different habitats were quantitatively analyzed using various solvents (water, methanol, ethanol, and n-hexane). The results showed that the methanol extracts exhibited the strongest 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging activity and the water extracts showed the best antioxidant activity in the 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) free radical scavenging activity, ferric reducing antioxidant power (FRAP), and reducing power (RP) assays. Moreover, the methanol extracts showed the best inhibitory activity against cholinesterase and HepG2 cancer cells. Correlation analysis revealed that the high antioxidant and anti-HepG2 cell proliferation activities were mainly attributed to the total phenolics, flavonoids, and proanthocyanidins while AChE inhibition was attributed to the total alkaloid and carotene content. The statistical results showed that the effect of habitats was lower than that of different solvents used. Additionally, the metabolic profiles of E. mollis leaves were evaluated using HPLC-ESI-Q TRAP-MS/MS, and a total of 1,017 chemical components were detected and classified into 23 classes. The organic acids and derivatives ranked the first, followed by flavone, amino acid and derivatives, and so on. In conclusion, the effects of different solvents were more significant than the effects of different habitats and the methanol extracts of E. mollis leaves could be used as an effective source of functional active components, provide benefits to physical health care and be applied to the food and pharmaceutical industries.