Cotton Appearance Grade Classification Based on Machine Learning

Lv, Yan; Rigall, Eric; Qi, Lin; Gao, Feng; Dong, Junyu

doi:10.1016/j.procs.2020.06.149

Cited by 6 publications

(23 citation statements)

References 11 publications

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“…By reducing the number of grades, the classification models' accuracies across all cultivars improved, as shown in Figure 6. Accuracies of greater than 95% were achieved by cultivars Giza 86, 87 and 94, which is on par with classification accuracies reported by other image models built to grade cotton lint [11]. The accuracy of the models built to grade Giza 90 and 96 were 89.89% and 77.31% respectively.…”

Section: Feature Analysissupporting

confidence: 59%

“…For the cultivar Giza 96, the highest classification accuracy achieved was 65.32% by the ensemble model, meaning a sensor developed using this model would misclassify the Giza 96 cotton lint samples 35 times out of 100. Classification models built to grade non-Egyptian cotton have achieved accuracies up to 98.9% [11]. However, these models were built to classify cotton lint into only seven grades, compared to the nine used to grade Egyptian cotton lint.…”

Section: Feature Analysismentioning

confidence: 99%

“…The grading is performed by manual inspection and is influenced by fibre colour and length, presence of "trash" (e.g., dried cotton leaves, seedcoats, barks, grass and dust) and maturity (age of plant harvested from) [10]. Manual inspection has several drawbacks including significant labour requirements, low inspection efficiency, and influence from inspection conditions such as light [11]. Previous work has explored developing decision support systems to improve, or replace, manual inspection.…”

Section: Introductionmentioning

confidence: 99%

“…This work aims to develop an intelligent sensor to grade Egyptian cotton lint from image measurements recorded by a low-cost optical sensor. Previous work has already demonstrated potential in this area [11]. Lv et al were able to develop a support vector machine ML model to classify the grade of cotton lint entering China based on the US upland grades (Good Middling, Strict Middling, Middling, Strict Low Middling, Low Middling, Strict Good Ordinary, Good Ordinary) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Previous work has already demonstrated potential in this area [11]. Lv et al were able to develop a support vector machine ML model to classify the grade of cotton lint entering China based on the US upland grades (Good Middling, Strict Middling, Middling, Strict Low Middling, Low Middling, Strict Good Ordinary, Good Ordinary) [11]. However, this work did not provide details on how and by whom the cotton lint samples were labelled in order to create a data set to train and validate the model [11], which is important to assess the validity of the final ML model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Intelligent Image Classification for Grading Egyptian Cotton Lint

Fisher¹,

Rady²,

El-Banna³

et al. 2021

Preprint

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Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

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Section: Feature Analysissupporting

confidence: 59%

Section: Feature Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Image Classification for Grading Egyptian Cotton Lint

Fisher¹,

Rady²,

El-Banna³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Combining Machine Learning and Handcrafted Image Processing to Identify Common Weeds in Cotton Fields

Priyanka,

Singh,

Gupta

et al. 2024

2024 2nd International Conference on Disruptive Technologies (ICDT)

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An image processing and machine learning solution to automate Egyptian cotton lint grading

Fisher

Rady

El-Banna

et al. 2022

Textile Research Journal

View full text Add to dashboard Cite

Egyptian cotton is one of the most important commodities for the Egyptian economy and is renowned globally for its quality, which is largely assessed and graded by manual inspection. This grading has several drawbacks, including significant labor requirements, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This work proposes a low-cost solution to replace manual inspection with classification models to grade Egyptian cotton lint using images captured by a charge-coupled device camera. While this method has been evaluated for classifying US and Chinese upland cotton staples, it has not been tested on Egyptian cotton, which has unique characteristics and grading requirements. Furthermore, the methodology to develop these classification models has been expanded to include image processing techniques that remove the influence of trash on color measurements and extract features that capture the intra-sample variance of the cotton samples. Three different supervised machine learning algorithms were evaluated: artificial neural networks; random forest; and support vector machines. The highest accuracy models (82.13–90.21%) used a random forest algorithm. The models’ accuracy was limited by the human error associated with labeling the cotton samples used to develop the classification models. Unsupervised machine learning methods, including k-means clustering, hierarchical clustering, and Gaussian mixture models, were used to indicate where labeling errors occurred.

show abstract

Cotton Appearance Grade Classification Based on Machine Learning

Cited by 6 publications

References 11 publications

Intelligent Image Classification for Grading Egyptian Cotton Lint

Intelligent Image Classification for Grading Egyptian Cotton Lint

Combining Machine Learning and Handcrafted Image Processing to Identify Common Weeds in Cotton Fields

An image processing and machine learning solution to automate Egyptian cotton lint grading

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