Technologies that can efficiently identify citrus diseases would assure fruit quality and safety and minimize losses for citrus industry.This research was aimed to investigate the potential of using color texture feat...Technologies that can efficiently identify citrus diseases would assure fruit quality and safety and minimize losses for citrus industry.This research was aimed to investigate the potential of using color texture features for detecting citrus peel diseases.A color imaging system was developed to acquire RGB images from grapefruits with normal and five common diseased peel conditions(i.e.,canker,copper burn,greasy spot,melanose,and wind scar).A total of 39 image texture features were determined from the transformed hue(H),saturation(S),and intensity(I)region-of-interest images using the color co-occurrence method for each fruit sample.Algorithms for selecting useful texture features were developed based on a stepwise discriminant analysis,and 14,9,and 11 texture features were selected for three color combinations of HSI,HS,and I,respectively.Classification models were constructed using the reduced texture feature sets through a discriminant function based on a measure of the generalized squared distance.The model using 14 selected HSI texture features achieved the best classification accuracy(96.7%),which suggested that it would be best to use a reduced hue,saturation and intensity texture feature set to differentiate citrus peel diseases.Average classification accuracy and standard deviation were 96.0%and 2.3%,respectively,for a stability test of the classification model,indicating that the model is robust for classifying new fruit samples according to their peel conditions.This research demonstrated that color imaging and texture feature analysis could be used for classifying citrus peel diseases under the controlled laboratory lighting conditions.展开更多
This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot(CBS).Hyperspectral images were taken of healthy fruit and those with CBS symptoms or othe...This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot(CBS).Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot,wind scar,or melanose.Spectral angle mapper(SAM)and spectral information divergence(SID)hyperspectral analysis approaches were used to classify fruit samples into two classes:CBS or non-CBS.The classification accuracy for CBS with SAM approach was 97.90%,and 97.14% with SID.The combination of hyperspectral images and two classification approaches(SID and SAM)have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions.The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.展开更多
The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European Union.This research aims to determine the detectable siz...The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European Union.This research aims to determine the detectable size limit for cankerous lesions using hyperspectral imaging approaches.Previously developed multispectral algorithms using visible to near-infrared wavelengths,were used to segregate cankerous citrus fruits from other peel conditions(normal,greasy spot,insect damage,melanose,scab and wind scar).However,this previous work did not consider lesion size.A two-band ratio method with a simple threshold based classifier(ratio of reflectance at wavelengths 834 nm and 729 nm),which gave maximum overall classification accuracy of 95.7%,was selected for lesion size estimation in this study.The smallest size of cankerous lesion detected in terms of equivalent diameter was 1.66 mm.The effect of variation of threshold values and number of erosion cycles(applying morphological erosion multiple times to the image)on estimation of smallest detectable lesion was observed.It was found that small threshold values gave better canker classification accuracies,while exhibiting a lower overall classification accuracy.Meanwhile,higher threshold values portrayed the opposite tendency.The threshold value of 1.275 gave the optimum tradeoff between canker classification accuracy,overall classification accuracy and minimal lesion size detection.Increasing the number of erosion cycles reduced detection rates of smaller canker lesions,leading to the conclusion that a single erosion cycle gave the best size estimation results.The erosion kernel of the size 3 mm×3 mm was used during the exploration.展开更多
文摘Technologies that can efficiently identify citrus diseases would assure fruit quality and safety and minimize losses for citrus industry.This research was aimed to investigate the potential of using color texture features for detecting citrus peel diseases.A color imaging system was developed to acquire RGB images from grapefruits with normal and five common diseased peel conditions(i.e.,canker,copper burn,greasy spot,melanose,and wind scar).A total of 39 image texture features were determined from the transformed hue(H),saturation(S),and intensity(I)region-of-interest images using the color co-occurrence method for each fruit sample.Algorithms for selecting useful texture features were developed based on a stepwise discriminant analysis,and 14,9,and 11 texture features were selected for three color combinations of HSI,HS,and I,respectively.Classification models were constructed using the reduced texture feature sets through a discriminant function based on a measure of the generalized squared distance.The model using 14 selected HSI texture features achieved the best classification accuracy(96.7%),which suggested that it would be best to use a reduced hue,saturation and intensity texture feature set to differentiate citrus peel diseases.Average classification accuracy and standard deviation were 96.0%and 2.3%,respectively,for a stability test of the classification model,indicating that the model is robust for classifying new fruit samples according to their peel conditions.This research demonstrated that color imaging and texture feature analysis could be used for classifying citrus peel diseases under the controlled laboratory lighting conditions.
文摘This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot(CBS).Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot,wind scar,or melanose.Spectral angle mapper(SAM)and spectral information divergence(SID)hyperspectral analysis approaches were used to classify fruit samples into two classes:CBS or non-CBS.The classification accuracy for CBS with SAM approach was 97.90%,and 97.14% with SID.The combination of hyperspectral images and two classification approaches(SID and SAM)have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions.The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.
文摘The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European Union.This research aims to determine the detectable size limit for cankerous lesions using hyperspectral imaging approaches.Previously developed multispectral algorithms using visible to near-infrared wavelengths,were used to segregate cankerous citrus fruits from other peel conditions(normal,greasy spot,insect damage,melanose,scab and wind scar).However,this previous work did not consider lesion size.A two-band ratio method with a simple threshold based classifier(ratio of reflectance at wavelengths 834 nm and 729 nm),which gave maximum overall classification accuracy of 95.7%,was selected for lesion size estimation in this study.The smallest size of cankerous lesion detected in terms of equivalent diameter was 1.66 mm.The effect of variation of threshold values and number of erosion cycles(applying morphological erosion multiple times to the image)on estimation of smallest detectable lesion was observed.It was found that small threshold values gave better canker classification accuracies,while exhibiting a lower overall classification accuracy.Meanwhile,higher threshold values portrayed the opposite tendency.The threshold value of 1.275 gave the optimum tradeoff between canker classification accuracy,overall classification accuracy and minimal lesion size detection.Increasing the number of erosion cycles reduced detection rates of smaller canker lesions,leading to the conclusion that a single erosion cycle gave the best size estimation results.The erosion kernel of the size 3 mm×3 mm was used during the exploration.
