Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables b...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/2/593 |
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| author | Muhammad Attique Khan Abdullah Alqahtani Aimal Khan Shtwai Alsubai Adel Binbusayyis M Munawwar Iqbal Ch Hwan-Seung Yong Jaehyuk Cha |
| author_facet | Muhammad Attique Khan Abdullah Alqahtani Aimal Khan Shtwai Alsubai Adel Binbusayyis M Munawwar Iqbal Ch Hwan-Seung Yong Jaehyuk Cha |
| author_sort | Muhammad Attique Khan |
| collection | DOAJ |
| description | Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance. |
| first_indexed | 2024-03-10T01:59:30Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:59:30Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-12757f9557a547a8a28d71a8152232e02023-11-23T12:49:35ZengMDPI AGApplied Sciences2076-34172022-01-0112259310.3390/app12020593Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature SelectionMuhammad Attique Khan0Abdullah Alqahtani1Aimal Khan2Shtwai Alsubai3Adel Binbusayyis4M Munawwar Iqbal Ch5Hwan-Seung Yong6Jaehyuk Cha7Department of Computer Science, HITEC University Taxila, Taxila 47080, PakistanCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer & Software Engineering, CEME NUST Rawalpindi, Rawalpindi 46000, PakistanCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaInstitute of Information Technology, Quaid-i-Azam University, Islamabad 44000, PakistanDepartment of Computer Science & Engineering, Ewha Womans University, Seoul 03760, KoreaDepartment of Computer Science, Hanyang University, Seoul 04763, KoreaAgriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.https://www.mdpi.com/2076-3417/12/2/593crops diseasesdata augmentationdeep learningentropyfeatures fusionmachine learning |
| spellingShingle | Muhammad Attique Khan Abdullah Alqahtani Aimal Khan Shtwai Alsubai Adel Binbusayyis M Munawwar Iqbal Ch Hwan-Seung Yong Jaehyuk Cha Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection Applied Sciences crops diseases data augmentation deep learning entropy features fusion machine learning |
| title | Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection |
| title_full | Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection |
| title_fullStr | Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection |
| title_full_unstemmed | Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection |
| title_short | Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection |
| title_sort | cucumber leaf diseases recognition using multi level deep entropy elm feature selection |
| topic | crops diseases data augmentation deep learning entropy features fusion machine learning |
| url | https://www.mdpi.com/2076-3417/12/2/593 |
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