An efficient method for the rapid detection of industrial paraffin contamination levels in rice based on hyperspectral imaging

Wang, Zhentao; Fu, Zuodong; Weng, Wuxiong; Yang, Dongze; Wang, Jinfeng

doi:10.1016/j.lwt.2022.114125

Cited by 7 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a more comprehensive understanding of this camera, refer to the research conducted by Wang et al. [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

Self Cite

View full text Add to dashboard Cite

Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.

show abstract

“…For a more comprehensive understanding of this camera, refer to the research conducted by Wang et al. [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Wang,

Chu,

Chen

et al. 2024

Plant Phenomics

Self Cite

View full text Add to dashboard Cite

show abstract

“…To maintain the accuracy of weed data sampling under different time of day and weather conditions, a white reference board was used for calibration (Wang et al 2023). Irradiance fluctuations brought on by atmospheric factors and changes in solar illumination are the most important factor in field reflectance measurements.…”

Section: Methodsmentioning

confidence: 99%

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Wang,

Chen,

et al. 2023

Weed Sci

Self Cite

View full text Add to dashboard Cite

Weeds contribute to biodiversity and a wide range of ecosystem functions. It is crucial to map different weed species and analyze their physiological activities. Remote sensing techniques for plant identification, especially hyperspectral imaging, are being developed using spectral response patterns to vegetation for detection and species identification. A library of hyperspectral images of 40 urban weed species in northeast China was established in this study. A terrestrial hyperspectral camera was used to acquire 435 hyperspectral images. The hyperspectral information for each weed species was extracted and analyzed. The spectral characteristics and vegetation indices of different weeds revealed the differences between weed species in the cities of northeast China and indirectly characterized the growth and physiological activity levels of different species, but could not effectively distinguish different species. Five methods—first derivative spectrum (FDS), second derivative spectrum (SDS), standard normal variate (SNV), moving averages (MA), and Savitzky-Golay (SG) smoothing—were used to pretreat the spectral curves to maximize the retention of spectral characteristics while removing the influence of noise. We investigated the application of a convolutional neural network (CNN) with terrestrial hyperspectral remote sensing to identify urban weeds in northeast China. A CNN classification model was established to distinguish weeds from the hyperspectral images and demonstrated a test accuracy of 95.32% to 98.15%. The accuracy of the original spectrum was 97.45%; SNV had the best accuracy (98.15%) and SG was the least accurate (95.32%). This provides a baseline for understanding the hyperspectral characteristics of urban weed species and monitoring their growth. It also contributes to the development of a hyperspectral imaging database with global applicability.

show abstract

“…SVR is a regression algorithm based on support vector machine. The objective is to determine the relationship between dependent variables and independent variables in a non-linear regression problem and to construct an approximate function that minimizes the prediction error for training cases [25] . During error minimization, the flatness of the function was maximized to decrease the risk of overfitting.…”

Section: Svr Modelmentioning

confidence: 99%

Model for estimating the weight-loss ratio of damaged Korla fragrant pears

Liu,

Tang,

Zhang

et al. 2024

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

To predict the weight-loss ratio of Korla fragrant pears effectively, improve commodity value and study the variation laws of the weight-loss ratio of damaged fragrant pears during storage, this study predicted the weight-loss ratio of fragrant pears by utilizing the generalized regression neural network (GRNN), support vector regression (SVR), partial least squares regression (PLSR) and error back propagation neural network (BPNN). The prediction performances of GRNN, SVR, PLSR and BPNN models were compared comprehensively, and the optimal model was determined. In addition, the optimal prediction model was verified. The results show that weight-loss ratio of fragrant pears increases gradually with the extension of storage time. During storage, the weight-loss ratio of fragrant pears is positively related to the degree of damage. The trained GRNN, SVR, PLSR and BPNN models can be used to predict the weight-loss ratio of fragrant pears. The BPNN model is the most accurate in predicting the weight-loss ratio of damaged fragrant pears (R 2 =0.9929; RMSE=0.2138). It has also been proved to have good predictive effect in production practice (R 2 =0.9377, RMSE=0.7138). The research findings can provide references to predict the delivery quality and time of delivery of Korla fragrant pears.

show abstract

An efficient method for the rapid detection of industrial paraffin contamination levels in rice based on hyperspectral imaging

Cited by 7 publications

References 31 publications

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Model for estimating the weight-loss ratio of damaged Korla fragrant pears

Contact Info

Product

Resources

About