An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to det...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/5/438 |
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| author | Ihtiram Raza Khan M. Siva Sangari Piyush Kumar Shukla Aliya Aleryani Omar Alqahtani Areej Alasiry M. Turki-Hadj Alouane |
| author_facet | Ihtiram Raza Khan M. Siva Sangari Piyush Kumar Shukla Aliya Aleryani Omar Alqahtani Areej Alasiry M. Turki-Hadj Alouane |
| author_sort | Ihtiram Raza Khan |
| collection | DOAJ |
| description | In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%. |
| first_indexed | 2024-03-10T23:00:53Z |
| format | Article |
| id | doaj.art-ee2f8d9c18824241937a4685272b9924 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T23:00:53Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-ee2f8d9c18824241937a4685272b99242023-11-19T09:44:30ZengMDPI AGBiomimetics2313-76732023-09-018543810.3390/biomimetics8050438An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease ClassificationIhtiram Raza Khan0M. Siva Sangari1Piyush Kumar Shukla2Aliya Aleryani3Omar Alqahtani4Areej Alasiry5M. Turki-Hadj Alouane6Department of Computer Science, Jamia Hamdard, Delhi 110062, IndiaDepartment of CSE, KPR Institute of Engineering and Technology, Coimbatore 641407, IndiaComputer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal 462033, IndiaCollege of Computer Science, King Khalid University, Abha 62529, Saudi ArabiaCollege of Computer Science, King Khalid University, Abha 62529, Saudi ArabiaCollege of Computer Science, King Khalid University, Abha 62529, Saudi ArabiaCollege of Computer Science, King Khalid University, Abha 62529, Saudi ArabiaIn recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.https://www.mdpi.com/2313-7673/8/5/438Artificial Rabbits AlgorithmAutomatic SegmentationHyper Parameter Optimizationleaf disease classificationsynthetic images |
| spellingShingle | Ihtiram Raza Khan M. Siva Sangari Piyush Kumar Shukla Aliya Aleryani Omar Alqahtani Areej Alasiry M. Turki-Hadj Alouane An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification Biomimetics Artificial Rabbits Algorithm Automatic Segmentation Hyper Parameter Optimization leaf disease classification synthetic images |
| title | An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification |
| title_full | An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification |
| title_fullStr | An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification |
| title_full_unstemmed | An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification |
| title_short | An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification |
| title_sort | automatic segmentation and hyper parameter optimization based artificial rabbits algorithm for leaf disease classification |
| topic | Artificial Rabbits Algorithm Automatic Segmentation Hyper Parameter Optimization leaf disease classification synthetic images |
| url | https://www.mdpi.com/2313-7673/8/5/438 |
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