Hyperspectral Data Compression Model Using SPCA (Segmented Principal Component Analysis) and Classification of Rice Crop Varieties

Shwetank,; Jain, Kamal; Bhatia, Karamjit

doi:10.1007/978-3-642-14834-7_34

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…More specifically, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two prominent feature extraction tools that have been greatly applied in the classification and quality inspection of rice seed using HSI [1],[5],[6], [20], [18]. PCA is a conventional technique commonly used in many HSI applications [42] and is noted to be the most regularly applied for dimensionality reduction in HSI-based rice seeds classifications (as shown in Table I). However, the work in [20] showed that LDA can give a better performance as a dimensionality reduction tool than PCA for rice seeds classification and quality inspection.…”

Section: Introductionmentioning

confidence: 99%

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Fabiyi,

Murray,

Zabalza

et al. 2024

IEEE Trans. Agri. Elect.

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Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often requires the use of dimensionality reduction techniques for the removal of redundant data. Folded Linear Discriminant Analysis (F-LDA) is an extension of Linear Discriminant Analysis (LDA, a commonly used technique for dimensionality reduction) and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This paper presents an improved version of F-LDA, exploring the feasibility of hybridizing a Genetic Algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with Principal Component Analysis (PCA), is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up 96.21%, this novel combination of GA and F-LDA (GA+F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%).

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Section: Introductionmentioning

confidence: 99%

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Fabiyi,

Murray,

Zabalza

et al. 2024

IEEE Trans. Agri. Elect.

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“…Several studies have been conducted on hyperspectral remote sensing and inversion of crop components. Some simulation models, which are feasible and widely applicable in various industries, have also been established till date (Takahashi et al, 2000 ; Chanseok et al, 2009 ; Shwetank et al, 2010 ; Onoyama et al, 2015 ; Marshall et al, 2016 ). Currently, R 780 /R 740 was considered as an optimal ratio index that established the uptake of nitrogen in maize above the ground (Mistele and Schmidhalter, 2008 , 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Tan

Zhou

et al. 2018

Front. Plant Sci.

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Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m−2 and 1.72 g·m−2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SDr − SDb)/(SDr + SDb), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SDr − SDb)/(SDr + SDb) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SDr − SDb)/(SDr + SDb). For diagnosing LNA in wheat, the newly normalized variable (SDr − SDb)/(SDr + SDb) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

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Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder

Kulkarni¹,

Wyawahare²,

Karwande³

et al. 2022

Communications in Computer and Information Science

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Hyperspectral Data Compression Model Using SPCA (Segmented Principal Component Analysis) and Classification of Rice Crop Varieties

Cited by 6 publications

References 12 publications

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat

Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder

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