A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the co...

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Veröffentlicht in:	Advances in human-computer interaction Jg. 2021; S. 1 - 15
Hauptverfasser:	Patil, Bhagya M., Burkpalli, Vishwanath
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Hindawi 2021 John Wiley & Sons, Inc Wiley
Schlagworte:	Accuracy Agricultural production Algorithms Analysis Bayesian analysis Classification Classifiers Color Corn Correlation coefficient Correlation coefficients Cotton Cotton industry Crop diseases Crop growth Crop yield Crop yields Data mining Datasets Decision trees Experimentation Feature extraction Image classification Image segmentation Learning algorithms Leaves Machine learning Medical imaging Multilayer perceptrons Neural networks Optimization algorithms Performance evaluation Plant diseases Skin cancer Support vector machines Texture Germany India
ISSN:	1687-5893, 1687-5907
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Abstract	Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.
AbstractList	Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.
Audience	Academic
Author	Burkpalli, Vishwanath Patil, Bhagya M.
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Copyright	Copyright © 2021 Bhagya M. Patil and Vishwanath Burkpalli. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Bhagya M. Patil and Vishwanath Burkpalli. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms	Accuracy Agricultural production Algorithms Analysis Bayesian analysis Classification Classifiers Color Corn Correlation coefficient Correlation coefficients Cotton Cotton industry Crop diseases Crop growth Crop yield Crop yields Data mining Datasets Decision trees Experimentation Feature extraction Image classification Image segmentation Learning algorithms Leaves Machine learning Medical imaging Multilayer perceptrons Neural networks Optimization algorithms Performance evaluation Plant diseases Skin cancer Support vector machines Texture
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Title	A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA
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