Digital image analysis of diverse Mexican rice cultivars

Camelo-Méndez, Gustavo A.; Camacho-Díaz, Brenda Hildeliza; Villar‐Martínez, Alma Angélica Del; Arenas-Ocampo, Martha Lucía; Bello‐Pérez, Luis A.; Jiménez–Aparicio, Antonio Ruperto

doi:10.1002/jsfa.5693

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…There have been many studies conducted with digital image processing technology on food quality control and evaluation (Teena et al 2013;Jackman et al 2009;Du and Sun 2005b). In terms of rice quality evaluation (Camelo-Méndez et al 2012;Shimizu et al 2008), the studies have been mainly conducted on evaluation of rice appearance quality (Camelo-Méndez et al 2012), the ratio of broken rice (Van Dalen 2004), grain shape (Bjork et al 2009), rice varieties (Liu et al 2005) and chalkiness ratio and chalkiness degree. Image analysis has also been used to determinate the size distribution and percentage of broken kernels of rice (Van Dalen 2004).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

et al. 2014

Food Anal. Methods

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

et al. 2014

Food Anal. Methods

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“…Méndez et al, 2013) was constructed for the interaction genotype/planting location and was based on the similarity percentage of K values obtained with Peleg's model. Groups were classified in 12 clusters.…”

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confidence: 99%

Modelling of water absorption in chickpea (Cicer arietinum L) seeds grown in México's northwest

Gómez-Favela¹,

García‐Armenta²,

Reyes‐Moreno³

et al. 2017

Rev.Mex.Ing.Quim.

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“…Peterson et al indicated that seed classification accuracy can be improved by using color images [25]. Image-based studies to discriminate between different varieties of rice grains [26,27], automatic classification of barley, oat, and rye [28], phenotyping of individual seeds [29], germination prediction of rice [30], and quality assessment of tomato seed [31,32] provide alternatives to manual inspection. However, the studies only focused on identification of species and not on the identification of healthy seeds or characterization of infected seeds along with inert materials.…”

Section: Introductionmentioning

confidence: 99%

Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Yasmin

Lohumi

Ahmed

et al. 2020

Sensors

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The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

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Digital image analysis of diverse Mexican rice cultivars

Abstract: Multivariate analysis indicated that the lateral side is the most sensitive for the classification of Mexican rice grains because of its color and morphometric characteristics. These results showed the application of image analysis for the future classifications of grains.

Cited by 13 publications

References 25 publications

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

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

Modelling of water absorption in chickpea (Cicer arietinum L) seeds grown in México's northwest

Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

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