Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm
This study presents a novel method for early-stage disease detection in pomegranate using a convolutional neural network (CNN) and honey badger optimization algorithm (HBOA). Existing fruit disease detection methods requires the appearance of external symptoms on the fruit surface. By the time sympt...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Food & Agriculture |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2401051 |
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| author | Sameera P Abhay A. Deshpande |
| author_facet | Sameera P Abhay A. Deshpande |
| author_sort | Sameera P |
| collection | DOAJ |
| description | This study presents a novel method for early-stage disease detection in pomegranate using a convolutional neural network (CNN) and honey badger optimization algorithm (HBOA). Existing fruit disease detection methods requires the appearance of external symptoms on the fruit surface. By the time symptoms appear on the fruit surface, disease spread inside the fruit will be considerably large, it will be difficult for farmers to implement counter measures to prevent disease spread. To overcome this problem, this study presents an early-stage disease detection method for pomegranates. Initially, image quality was enhanced using contrast limited adaptive histogram equalization. Pre-processed image was segmented using k-means clustering. The features for early-stage disease detection are color-based, region-based and texture-based. The segmented image was subjected to feature extraction based on the identified features. The CNN is modified in which the extracted image features are given as input, and the modified CNN classifies the pomegranate into healthy and early-stage disease affected fruit. Classification accuracy was enhanced using the HBOA algorithm. The proposed CNN-HBOA model achieved a classification accuracy of 93.29%. To test the superiority of CNN-HBOA, early-stage disease detection was performed with existing state-of-the-art classifiers. The proposed CNN-HBOA outperformed existing classifiers with better classification accuracy. |
| format | Article |
| id | doaj-art-678fc003b9984bb7b1ab15f096e43d17 |
| institution | DOAJ |
| issn | 2331-1932 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj-art-678fc003b9984bb7b1ab15f096e43d172025-08-20T02:49:48ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322024-12-0110110.1080/23311932.2024.2401051Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithmSameera P0Abhay A. Deshpande1Department of Electronics and Communication, RV College of Engineering, Bangalore, IndiaDepartment of Electronics and Communication, RV College of Engineering, Bangalore, IndiaThis study presents a novel method for early-stage disease detection in pomegranate using a convolutional neural network (CNN) and honey badger optimization algorithm (HBOA). Existing fruit disease detection methods requires the appearance of external symptoms on the fruit surface. By the time symptoms appear on the fruit surface, disease spread inside the fruit will be considerably large, it will be difficult for farmers to implement counter measures to prevent disease spread. To overcome this problem, this study presents an early-stage disease detection method for pomegranates. Initially, image quality was enhanced using contrast limited adaptive histogram equalization. Pre-processed image was segmented using k-means clustering. The features for early-stage disease detection are color-based, region-based and texture-based. The segmented image was subjected to feature extraction based on the identified features. The CNN is modified in which the extracted image features are given as input, and the modified CNN classifies the pomegranate into healthy and early-stage disease affected fruit. Classification accuracy was enhanced using the HBOA algorithm. The proposed CNN-HBOA model achieved a classification accuracy of 93.29%. To test the superiority of CNN-HBOA, early-stage disease detection was performed with existing state-of-the-art classifiers. The proposed CNN-HBOA outperformed existing classifiers with better classification accuracy.https://www.tandfonline.com/doi/10.1080/23311932.2024.2401051Pomegranateconvolutional neural networkhoney badger optimization algorithmearly-stage disease detectionclassification accuracyArtificial Intelligence |
| spellingShingle | Sameera P Abhay A. Deshpande Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm Cogent Food & Agriculture Pomegranate convolutional neural network honey badger optimization algorithm early-stage disease detection classification accuracy Artificial Intelligence |
| title | Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm |
| title_full | Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm |
| title_fullStr | Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm |
| title_full_unstemmed | Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm |
| title_short | Efficient early-stage disease detection in pomegranate (Punica granatum) using convolutional neural networks optimized by honey badger optimization algorithm |
| title_sort | efficient early stage disease detection in pomegranate punica granatum using convolutional neural networks optimized by honey badger optimization algorithm |
| topic | Pomegranate convolutional neural network honey badger optimization algorithm early-stage disease detection classification accuracy Artificial Intelligence |
| url | https://www.tandfonline.com/doi/10.1080/23311932.2024.2401051 |
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