Aspect Ratio Analysis Using Image Processing for Rice Grain Quality

Aggarwal, Amit K; Mohan, Ratan

doi:10.2202/1556-3758.1788

Cited by 27 publications

(10 citation statements)

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“…To address this issue, the contributions of L * , a * , b * , h, C * , S L* , S a* , S b* , S h and S C* should be calculated. Table 4 presents the contribution values of L * , a * , b * , h, C * ,S L* , S a* , S b* , S h and S C* of the out-of-control packs of sausages (i. e. packs 1,2,3,8,17,24,28). For instance, ΔL * is the difference in lightness or darkness values.…”

Section: Implication Of Control Chart Signalsmentioning

confidence: 99%

“…In addition, Gökmen and Sügüt applied computer vision based approach for the measurement of color in a user defined polygonal area on the digital image of a food product [7]. Aggarwal and Mohan analyzed aspect ratio of rice grain quality using computer vision [8]. Girolami et al utilized image analysis with a computer vision system and then with a consumer test in evaluating the appearance of Lucanian dry sausages.…”

Section: Introductionmentioning

confidence: 99%

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Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Kaewsuwan

Yuangyai

Cheng

et al. 2016

International Journal of Food Engineering

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Sausage color usually influences consumers' selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. "within pack" variation) and uniformity (i. e. "between-pack" variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L * , a * , b * , hue (h) and chroma (C * ). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T 2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.

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Section: Implication Of Control Chart Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Kaewsuwan

Yuangyai

Cheng

et al. 2016

International Journal of Food Engineering

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show abstract

“…The rice kernels were classified into short, long, slender, round and bold grades using a support vector machine classifier using variations in length, width and its ratio of individual seeds as the classification criteria. Based on relative differences in kernel sizes, the proposed technique could also differentiate head rice from brokens and brewers 8 . In another study, the visual grading of soybean was described using image analysis techniques taking size uniformity as the classification criteria 9 .…”

Section: Introductionmentioning

confidence: 99%

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

Sharma

Kumar

Alam

2021

Sci Rep

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The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

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“…Looking into the broader domain, there is a large amount of research into measuring the quality of other crops. Firstly, the grades of product are determined by calculating rice kernel shape and size features and training a support vector machine [7,8]. Additionally, in [9] rice colour features and Fourier descriptors for shape and size are extracted from which the quality grade is determined through multivariate statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

Rasmussen

Moeslund

2019

Sensors

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Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the adoption of two deep learning-based methods for kernel processing prediction without the cumbersome step of separating kernels and stover before capturing images. The methods show that kernels can be detected both with bounding boxes and at pixel-level instance segmentation. Networks were trained on up to 1393 images containing just over 6907 manually annotated kernel instances. Both methods showed promising results despite the challenging setting, with an average precision at an intersection-over-union of 0.5 of 34.0% and 36.1% on the test set consisting of images from three different harvest seasons for the bounding-box and instance segmentation networks respectively. Additionally, analysis of the correlation between the Kernel Processing Score (KPS) of annotations against the KPS of model predictions showed a strong correlation, with the best performing at r(15) = 0.88, p = 0.00003. The adoption of deep learning-based object recognition approaches for kernel processing measurement has the potential to lower the quality assessment process to minutes, greatly aiding a farmer in the strenuous harvesting season.

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Aspect Ratio Analysis Using Image Processing for Rice Grain Quality

Cited by 27 publications

References 0 publications

Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

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