Research on variety identification of common bean seeds based on hyperspectral and deep learning.

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties.

Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion.

Germination rate is important for seed selection and planting and quality. In this study, hyperspectral image technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sugarbeet seeds. In this study, we proposed a nondestructive prediction method for sugarbeet seed germination.

Convolutional neural network for apple bruise detection based on hyperspectral.

The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper.

Intelligent detection of hard seeds of snap bean based on hyperspectral imaging.

As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean.

Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.

How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processed by binarization, morphology, contour extraction and so on. The average spectrum of the single seed image was obtained by image segmentation.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique.

A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm.

Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging.

Background: Bruising time of apple is one of the most important factors for internal quality assessment. The present study aimed to establish a non-destructive method for the classification of apple bruising time using visible and near-infrared (VNIR) hyperspectral imaging. In this study, VNIR hyperspectral images were obtained and analyzed at seven bruising periods.

Application of Visible/Near-Infrared Spectroscopy in the Prediction of Azodicarbonamide in Wheat Flour.

Unlabelled: Azodicarbonamide is wildly used in flour industry as a flour gluten fortifier in many countries, but it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour. Applying a rapid, convenient, and noninvasive technique in food analytical procedure for the safety inspection has become an urgent need. This paper used Vis/NIR reflectance spectroscopy analysis technology, which is based on the physical property analysis to predict the concentration of azodicarbonamide in flour.

Non-destructive prediction of protein content in wheat using NIRS.

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples.

[Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy].

Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples.