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

Min Huang,Jianwei Qin,Chujie He,Qibing Zhu

doi:10.3390/app6060183

Min Huang, Jianwei Qin + Show 2 more

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https://doi.org/10.3390/app6060183

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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.

Highlights

Maize (Zea mays), known as corn, is a major source of food, forages, fuel, and industrial materials [1]
11 spectral, 55 first-order statistics (5 ˆ 11), 88 second-order statistics (8 ˆ 11), and 5 morphologic spectral, 55 first‐order statistics (5 × 11), 88 second‐order statistics (8 × 11), and 5 morphologic features, were obtained. When these 159 features were used as the input of the least squares–support vector machines (LS–Support vector machine (SVM)), the features, were obtained. When these 159 features were used as the input of the LS–SVM, the classifier had the risk of classification accuracy reduction resulting from the dimension disaster classifier had the risk of classification accuracy reduction resulting from the dimension disaster caused by feature redundancy and noise contained in the features
The 60 principal components obtained by principle component analysis (PCA) or multidimensional scaling (MDS) were employed for developing the LS–SVM classification model

Summary

Introduction

Maize (Zea mays), known as corn, is a major source of food, forages, fuel, and industrial materials [1]. The number of maize seed varieties has considerably increased because of the extensive application of seed hybrid technology. In the process of cultivation, harvesting, storage, and transportation, each production procedure may lead to variety mixing, thereby complicating seed classification and decreasing crop yield. Variety classification or identification before planting is important for maize seeds. Several traditional methods for maize seed classification have been developed over the past years and vary from each other, such as in morphology method, protein electrophoresis, and DNA molecular marker technology. Most of these methods require professional staff and specialized instruments, and they are often time consuming [3]. The traditional method is convenient and economic, its accuracy depends on the experience of the inspectors and is influenced by subjective

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