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Abstract: The mudflats along the east coast of China can be important alternative sources for arable lands after amendment. The newly reclaimed mudflats as typical saline soils are not suitable for cultivation owing to high salinity and nutrient deficiencies. The key to amending mudflat saline soils into fertile soils is to reduce salinity in the top layer of soil. Salinity reduction is usually accomplished through rainfall and freshwater irrigation. However, lack of good soil aggregation status might attribute to salt solution rise through soil capillary. Increasing soil organic matter promoted the formation of organo‐mineral complexes and clustered soil aggregates, which is a prerequisite of hindering re‐salinization, improving fertility and facilitating the evolution of coastal mudflat soil into agricultural soil. Research on the formation of clustered soil aggregates during the evolution of mudflat soil into fertile soil should focus on the formation mechanism, influencing factors and regulatory mechanism of organo‐mineral complexes in the early stage after organic matter enters the soil. In addition, exploration of the effects of the special properties of mudflat soil, for example, high saline, high pH and low levels of clay particles, on the formation and stability of organo‐mineral complexes in soil is necessary. Research on this topic can provide scientific evidences to efficiently utilize organic resources and accelerate the evolution of mudflat soil into agricultural soil. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to conquer the difficulty of simultaneously recognizing the multiple defects of potatoes samples placed randomly, this paper proposed a non-destructive detection method which combined manifold learning dimension reduction algorithm based on hyperspectral information fusion and extreme learning machine (ELM) to simultaneously distinguish the multiple defects of potatoes. In this paper, 367 potatoes were picked which were made up of 111 sprouting potatoes, 90 green rind potatoes, 46 blackheart potatoes and 120 normal potatoes. The hyperspectral image acquisition system contained imaging spectroradiometer (SPECIM, V10E, Finland), data acquisition box, lighting system, electric moving stage and objective table. The reflection hyperspectral information of all those potatoes was acquired by using the hyperspectral image acquisition system, whose spectral wavelength ranged from 390 to 1040 nm. The hyperspectral information included the spectral information from 520 wave bands and the imaging information from 520 gray images. After the correction to hyperspectral data with the standard black and white board, hyperspectral data in the range of 450-990 nm, which had high signal-to-noise ratio (SNR), were selected as original spectrum for subsequent processing. To deal with the spectral information, the average spectrum was abstracted from the region of interests (ROI) on every potato sample by using the environment for visualizing images (ENVI). Comparing several data preprocessing methods, detrend was determined as the optimal spectral preprocessing method. Diffusion maps (DM), locally linear embedding (LLE) and hessian locally linear embedding (HLLE) were respectively utilized for the purpose of cutting down the dimension of spectrum data after the spectral preprocessing named detrend. To deal with the hyperspectral imaging information, every pseudo-color image of potatoes was morphologically processed before extracting 84 image texture characteristics based on gray level co-occurrence matrix (GLCM). By using successive projections algorithm (SPA), 10 texture features were properly selected, which consisted of homogeneity of 45° GLCM in R gray image, energy of 90° GLCM in R gray image, correlation of 90° GLCM in R gray image, contrast of 135° GLCM in R gray image, correlation of 0° GLCM in G gray image, contrast of 0° GLCM in G gray image, correlation of 90° GLCM in G gray image, homogeneity of 45° GLCM in B gray image, correlation of 90° GLCM in B gray image and contrast of 135° GLCM in B gray image. After the fusion of the spectral characteristics and image features, the 367 potato samples were divided into calibration set and test set. The calibration set had 250 potato samples including 74 sprouting potatoes, 59 green rind potatoes, 28 blackheart potatoes and 89 normal potatoes. The whole number of samples in test set was 117 which contained 37 sprouting potatoes, 31 green rind potatoes, 18 blackheart potatoes and 31 normal potatoes. Six models were respectively established based on support vector machine (SVM) and ELM, which were DM-SVM, LLE-SVM, HLLE-SVM, DM-ELM, LLE-ELM and HLLE-ELM. Comparing and analyzing the results of the 6 models and the spending time, we discovered that the DM was the best manifold learning dimension reduction algorithm to deduce the spectral information. We alsofound that the DM-ELM model was the best model because of the better recognition rate for recognizing the multiple defects of potatoes and spending less time. For the DM-ELM model, the single recognition rate of sprouting potatoes, green rind potatoes, blackheart potatoes and normal potatoes respectively reached 97.30%, 93.55%, 94.44% and 100%, and the mixed recognition rate reached 96.58%. It only cost 0.11 s to build the DM-ELM model. The results indicate that combining hyperspectral information fusion with manifold learning dimension reduction algorithm can simultaneously distinguish the multiple defects of potatoes. [ABSTRACT FROM AUTHOR]