Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging

Abstract

Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares-support vector machines (LS-SVM) model. The LS-SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy.

Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm

“…In the past, selection of corn varieties possessing high lodging resistance depended on counting lodged plants by harvest [4]. This method, however, is disadvantageous, as a high coefficient of variation is frequently produced by various uncontrolled environmental factors such as wind and rain storms [5][6][7]. For instance, a harsh wind storm may lead to flattening of a whole corn field irrespective of the lodging resistance of stalks in the field, and therefore makes it hard to discriminate stronger varieties from weaker ones [8][9][10].…”

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

“…The spectral analysis method reflects the difference in the internal physical structure and the chemical composition of seed varieties through spectral information, which has the advantages of being fast, accurate, and nondestructive [12]; it has been applied to multiple tasks related to seeds, such as the identification of soybean [13,14], rice [6] and maize [15][16][17] seed varieties, the identification of nontransgenic and transgenic seeds [18][19][20], the identification of seed geographical sources [21], the identification of infected germ seeds [22], the identification of infected pest seeds and healthy seeds [23], identification of the year of seeds [24,25], and the determination of tomato [26], soybean [27,28], corn [28], muskmelon [29], cabbage and radish [30] seed vitality. Less research has been performed on image analysis than spectral analysis, and only some plants have been studied through image analysis, such as peppers [31], paddy [32,33], and corn [34].…”

Multispectral Imaging for Plant Food Quality Analysis and Visualization

“…By extracting spectra and image information, the discrimination of waxy corn varieties was achieved with over 96.3% accuracy based on the SPA‐SVM model (Yang and others ). To classify different varieties of maize seeds, multispectral imaging in combination with the SPA‐LS‐SVM model developed with 11 variables obtained more than a 92% classification accuracy (Huang and others ). To further optimize the classification of varieties of maize seeds, Wang and others () reduced the 11 feature wavelengths to almost half that number.…”