Corn Whiteness Measurement and Classification Using Machine Vision

Liu, J.; Paulsen, Marvin R

doi:10.13031/2013.2759

Cited by 23 publications

(3 citation statements)

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“…The germ area is usually white. We used a well-designed method to separate the germ area from corn [26]. As shown in Figure 8, a polygon is used to estimate the germ area.…”

Section: Feature Extractionmentioning

confidence: 99%

Corn Classification System based on Computer Vision

Dai

Sun

et al. 2019

Symmetry

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Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.

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“…The germ area is usually white. We used a well-designed method to separate the germ area from corn [26]. As shown in Figure 8, a polygon is used to estimate the germ area.…”

Section: Feature Extractionmentioning

confidence: 99%

Corn Classification System based on Computer Vision

Dai

Sun

et al. 2019

Symmetry

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show abstract

“…At present, machine vision technology and spectroscopy technology are widely used in agricultural product detection due to their non-destructive, rapid, and reliable characteristics. Machine vision technology can grade samples by analyzing the spatial information of samples and extracting relevant characteristics of agricultural product quality [6,7,8,9]. Machine vision technology can effectively detect the external quality of agricultural products (e.g., shape, color, etc.).…”

Section: Introductionmentioning

confidence: 99%

Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Zhu

Liu

Bao

et al. 2019

Foods

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Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

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“…In YcrCb color modle, mean values of color features was computed and whiteness of corn kernel was extracted by J．Liu. The research proposed a whiteness definition method, which is fast, accurate and simple [2] . Four corns (xhg, xn12,wn14 and white corn) were studied by Yang Shuqin.…”

Section: Introductionmentioning

confidence: 99%

Study on the Intelligent Recognition Method for the Maturity Grade of Fresh Corn Ear Based on Computer Vision

Wang

Sun

et al. 2013

AMM

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In order to realize the intelligent recognition for the maturity grade of fresh corn ear, intelligent inspection system was studied based on computer vision, which could automatically complete the collection and handling of ear graphic and the recognition of maturity of the corn ear. Based on the study, a kind of intelligent recognition method was put forward under the graphic of certain frequency domain. An energy chain was established, and the characters of energy spectrum was extracted through the two-dimensional inverse discrete Fourier transformation on the graphics collected. With the above characters, a probabilistic neural network was developed, the accuracy rate of the recognition method could be 96.7%.

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Corn Whiteness Measurement and Classification Using Machine Vision

Cited by 23 publications

References 0 publications

Corn Classification System based on Computer Vision

Corn Classification System based on Computer Vision

Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Study on the Intelligent Recognition Method for the Maturity Grade of Fresh Corn Ear Based on Computer Vision

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