Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms

Khoje, Suchitra

doi:10.1111/jtxs.12284

“…The prepared image blocks were transferred from RGB into HSV and L*a*b colour spaces using conversion functions of "rgb2hsv" and "rgb2lab" in MATLAB software [27,49,50], and the average and standard deviation values of Red, Green, Blue, Hue, Saturation, Value, Luminance, a* and b* colour components were extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Bakhshipour

¹

,

Zareiforoush

²

2020

Preprint

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A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

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“…Original field-captured Images were loaded in Photoshop and the blocks were extracted carefully from different regions of the desired plants using "marquee tool" in the Photoshop toolbar, without any change in resolution and color values of images. Region of interest selection using different region selection tools of Photoshop software have been reported in several image processing related researches [44, 47, The prepared image blocks were transferred from RGB into HSV and L*a*b color spaces using conversion functions of "rgb2hsv" and "rgb2lab" in MATLAB software [27,49,50], and the average and standard deviation values of Red, Green, Blue, Hue, Saturation, Value, Luminance, a* and b* color components were extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Comprehensive information about wavelet transform and its applications in images, are provided by Vyas et al [25] and Kolekar et al [26]. Applications of DWT were reported for several agro-food related areas [11,24,27,28].…”

Section: Introductionmentioning

confidence: 99%

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Bakhshipour

¹

,

Zareiforoush

²

2020

View full text Add to dashboard Cite

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

show abstract

“…The prepared image blocks were transferred from RGB into HSV and L*a*b colour spaces using conversion functions of "rgb2hsv" and "rgb2lab" in MATLAB software [27,44,45], and the average and standard deviation values of Red, Green, Blue, Hue, Saturation, Value, Luminance, a* and b* colour components were extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Comprehensive information about wavelet transform and its applications in images, are provided by Vyas et al [25] and Kolekar et al [26]. Applications of DWT were reported for several agro-food related areas [11,24,27,28].…”

Section: Introductionmentioning

confidence: 99%

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Bakhshipour

¹

,

Zareiforoush

²

2020

Preprint

0

View full text Add to dashboard Cite

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

show abstract

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms

Cited by 17 publications

References 16 publications

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

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