Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Abstract

This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', lengthwidth ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice.

Spatial and spectral features utilization on a Hyperspectral imaging system for rice seed varietal purity inspection

“…The authors in [10] give a broad range of HSI applications for beef, pork, fruits, and plant products quality evaluations. For the rice grain quality inspection, [11] used a range of VIS/NIR spectral (400-1000 nm) information for discriminating three rice varieties. By using Principle Component Analysis (PCA) and Back Propagation Neural Network (BPNN), they achieved a classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively.…”

Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging

“…The details of the system were the same as described previously [24] . The system was made up of hardware components and data acquisition software.…”

Multispectral Imaging for Plant Food Quality Analysis and Visualization

“…In addition, a multispectral imaging technique could be utilized to assess quality attributes of rice, tomato seed, oat kernel, and tubers. The discrimination accuracies of variety, chalkiness degree, and shape features of rice kernels from different regions reached over 89.91% based on the BPNN model with 7 optimal spectral variables (Wang and others ). The SVM model together with wavelength selection method of PCA achieved the highest accuracy (91.67%) for the rapid identification of rice origin using multispectral imaging (Sun and others ).…”