BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model
In the task of identification of infected agriculture plants, the leaf-based disease identification technique is especially effective in better understand crop disease among various techniques to detect infection. Recognition of an infected leaf image from healthy images gets encumbered when the mo...
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| Format: | Article |
| Language: | English |
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Taiwan Association of Engineering and Technology Innovation
2024-02-01
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| Series: | Proceedings of Engineering and Technology Innovation |
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| Online Access: | https://ojs.imeti.org/index.php/PETI/article/view/13352 |
| _version_ | 1797276800989003776 |
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| author | Vijay Choudhary Archana Thakur |
| author_facet | Vijay Choudhary Archana Thakur |
| author_sort | Vijay Choudhary |
| collection | DOAJ |
| description |
In the task of identification of infected agriculture plants, the leaf-based disease identification technique is especially effective in better understand crop disease among various techniques to detect infection. Recognition of an infected leaf image from healthy images gets encumbered when the model is required to detect the type of leaf disease. This paper presents a BAT-based crop disease prediction bootstrap model (BCDPBM) that identifies the health of the leaf and performs disease prediction. The BAT algorithm in the proposed model increases the capability of the Gaussian mixture model for foreground region detection. Furthermore, in the work, the co-occurrence matrix feature and histogram feature are extracted for the training of the bootstrap model. Hence, leaf foreground detection by the BAT algorithm with the Gaussian mixture improves the feature extraction quality for bootstrap learning. The proposed model utilizes a dataset of real leaf images for conducting experiments. The results of the model are compared with different existing models across various parameters. The results show the prediction accuracy enhancement of multiclass leaf disease using the BCDPBM model.
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| first_indexed | 2024-03-07T15:33:33Z |
| format | Article |
| id | doaj.art-4d7496531f8b4de6ae0c6e88c06dce05 |
| institution | Directory Open Access Journal |
| issn | 2413-7146 2518-833X |
| language | English |
| last_indexed | 2024-03-07T15:33:33Z |
| publishDate | 2024-02-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | Proceedings of Engineering and Technology Innovation |
| spelling | doaj.art-4d7496531f8b4de6ae0c6e88c06dce052024-03-05T13:17:12ZengTaiwan Association of Engineering and Technology InnovationProceedings of Engineering and Technology Innovation2413-71462518-833X2024-02-012610.46604/peti.2024.13352BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model Vijay Choudhary0Archana Thakur1Institute of Engineering and Technology, DAVV/ IPS Academy, Institute of Engineering and Science, Indore, IndiaSchool of Computer Science & IT, Devi Ahilya University, Indore, India In the task of identification of infected agriculture plants, the leaf-based disease identification technique is especially effective in better understand crop disease among various techniques to detect infection. Recognition of an infected leaf image from healthy images gets encumbered when the model is required to detect the type of leaf disease. This paper presents a BAT-based crop disease prediction bootstrap model (BCDPBM) that identifies the health of the leaf and performs disease prediction. The BAT algorithm in the proposed model increases the capability of the Gaussian mixture model for foreground region detection. Furthermore, in the work, the co-occurrence matrix feature and histogram feature are extracted for the training of the bootstrap model. Hence, leaf foreground detection by the BAT algorithm with the Gaussian mixture improves the feature extraction quality for bootstrap learning. The proposed model utilizes a dataset of real leaf images for conducting experiments. The results of the model are compared with different existing models across various parameters. The results show the prediction accuracy enhancement of multiclass leaf disease using the BCDPBM model. https://ojs.imeti.org/index.php/PETI/article/view/13352image processingleaf disease predictionco-occurrence matrix (CCM)machine learning |
| spellingShingle | Vijay Choudhary Archana Thakur BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model Proceedings of Engineering and Technology Innovation image processing leaf disease prediction co-occurrence matrix (CCM) machine learning |
| title | BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model |
| title_full | BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model |
| title_fullStr | BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model |
| title_full_unstemmed | BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model |
| title_short | BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model |
| title_sort | bat algorithm based multi class crop leaf disease prediction bootstrap model |
| topic | image processing leaf disease prediction co-occurrence matrix (CCM) machine learning |
| url | https://ojs.imeti.org/index.php/PETI/article/view/13352 |
| work_keys_str_mv | AT vijaychoudhary batalgorithmbasedmulticlasscropleafdiseasepredictionbootstrapmodel AT archanathakur batalgorithmbasedmulticlasscropleafdiseasepredictionbootstrapmodel |