The Experts below are selected from a list of 2232 Experts worldwide ranked by ideXlab platform
Bin Li - One of the best experts on this subject based on the ideXlab platform.
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multispectral detection of skin defects of bi colored peaches based on vis nir hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Jiangbo Li, Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Bin LiAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
Jiangbo Li - One of the best experts on this subject based on the ideXlab platform.
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multispectral detection of skin defects of bi colored peaches based on vis nir hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Jiangbo Li, Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Bin LiAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
Liping Chen - One of the best experts on this subject based on the ideXlab platform.
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multispectral detection of skin defects of bi colored peaches based on vis nir hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Jiangbo Li, Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Bin LiAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
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Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Shuxiang Fan, Li BinAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
Wenqian Huang - One of the best experts on this subject based on the ideXlab platform.
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multispectral detection of skin defects of bi colored peaches based on vis nir hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Jiangbo Li, Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Bin LiAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
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Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Shuxiang Fan, Li BinAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
Qingyan Wang - One of the best experts on this subject based on the ideXlab platform.
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multispectral detection of skin defects of bi colored peaches based on vis nir hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Jiangbo Li, Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Bin LiAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
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Multispectral detection of skin defects of bi-colored peaches based on vis–NIR hyperspectral imaging
Postharvest Biology and Technology, 2016Co-Authors: Liping Chen, Wenqian Huang, Qingyan Wang, Baohua Zhang, Xi Tian, Shuxiang Fan, Li BinAbstract:Abstract Fruit skin defects may cause Fruit Spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of Fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored Fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other Fruit.
