基于冠层光谱的水稻穗颈瘟病害程度预测模型

对水稻稻瘟病病害程度的定量预测是精准防控的关键,田间冠层尺度的研究可为高光谱传感器提供理论基础.以受穗颈瘟胁迫的水稻为研究对象,采用SVC HR768i型光谱辐射仪在大田中获取灌浆期两个不同时间段的水稻冠层光谱反射率,以水稻发病株数百分比作为病害严重程度指标.冠层光谱数据采用九点平滑预处理,并重采样为1 nm间隔,计算...     

基于拉曼光谱的大豆细菌性病害标志物研究

大豆在生长过程中因病害影响其产量会急剧下降,如果不及时判别出病害种类,喷洒相关农药,病害严重的大豆甚至会绝产.及时判别病害种类进行合理施药,阻止病害进一步发展是保证大豆安全生产的重要环节.目前,基于大豆植株细菌性病害的病原菌鉴定和聚合酶链式反应(polymerase chain reaction,PCR)的鉴定方法,最...     

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy (FIBS) combined with machine learning algorithms was used to identify five aluminum alloys. To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys, principal component analysis (PCA) combined with support vector machine (SVM) and K-nearest neighbor (KNN) was used. The intensity and intensity ratio of fifteen lines of six elements (Fe, Si, Mg, Cu, Zn, and Mn) in the FIBS spectrum were selected. The distances between the focusing lens and the target surface in the pre-filament, filament, and post-filament were 958 mm, 976 mm, and 1000 mm, respectively. The source data set was fifteen spectral line intensity ratios, and the cumulative interpretation rates of PC1, PC2, and PC3 were 97.22%, 98.17%, and 95.31%, respectively. The first three PCs obtained by PCA were the input variables of SVM and KNN. The identification accuracy of the different positions of focusing lens and target surface was obtained, and the identification accuracy of SVM and KNN in the filament was 100% and 90%, respectively. The source data set of the filament was obtained by PCA for the first three PCs, which were randomly selected as the training set and test set of SVM and KNN in 3:2. The identification accuracy of SVM and KNN was 97.5% and 92.5%, respectively. The research results can provide a reference for the identification of aluminum alloys by FIBS.     

Au聚体的吸附位点对杀草强分子表面增强拉曼光谱的影响研究

杀草强是一种白色结晶粉末状的化学除草剂,对环境有极强的破坏性,大量使用会造成农残污染,对生物体具有致癌作用。目前利用密度泛函理论探究杀草强分子的拉曼增强机理的相关研究相对较少,开展了Au聚体吸附位点对杀草强分子表面增强拉曼光谱的影响研究。采用Multiwfn软件结合VMD软件探究了杀草强分子表面静电势分布,得出N₁, N₄和N₆是杀草强分子与Au原子配位的最佳位置。基于密度泛函理论,运用GaussView5.0和Gaussian09软件,在B3LYP/6-31++G(d, p)基组水平上对杀草强分子进行几何构型优化,并对C, H, N原子使用6-31++G(d, p)基组,Au原子使用LANL2DZ赝式基组,计算了杀草强分子的常规拉曼散射光谱和杀草强分子与Au₄聚体以及Au₆聚体吸附的表面增强拉曼散射光谱,并进行特征峰指认和比较。结果发现在Au与N₁配位形成的复合物中,在1 064, 1 200, 1 392和1 592 cm^-1处杀...