Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm
Fruit cultivation plays a pivotal role in improvement of the agricultural economy. Pomegranate is a nutritionally rich fruit that is highly valuable because of its excellent antioxidant properties, richness in vitamins and fiber. Pomegranate is affected by diseases, which can severely affect its yie...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Food Properties |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2024.2365927 |
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| _version_ | 1846163912218640384 |
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| author | Sameera P Abhay A Deshpande |
| author_facet | Sameera P Abhay A Deshpande |
| author_sort | Sameera P |
| collection | DOAJ |
| description | Fruit cultivation plays a pivotal role in improvement of the agricultural economy. Pomegranate is a nutritionally rich fruit that is highly valuable because of its excellent antioxidant properties, richness in vitamins and fiber. Pomegranate is affected by diseases, which can severely affect its yield and quality. Timely disease detection is vital for effective disease control in pomegranate orchards. This study presents a novel approach for disease detection in pomegranate and categorization of pomegranate using a hybrid Convolutional Neural Network (CNN) coupled with the Honey Badger Optimization Algorithm (HBOA). The proposed hybrid CNN-HBOA approach leverages the strengths of both CNN and metaheuristic optimization algorithm HBOA. The CNN model was designed to extract high-level features from images of pomegranate affected by various diseases, and HBOA was employed to fine-tune the CNN parameters to improve the accuracy and robustness of the model. Initially, the quality of the images was enhanced using a contrast enhancement technique, and the images were segmented using the Detectron2 algorithm. Finally, the extracted features were fed as input to the disease detection stage wherein the CNN classifies the disease. Furthermore, the classification accuracy of the CNN model was enhanced using the HBOA algorithm. The performance of the hybrid CNN-HBOA algorithm was evaluated using a vast collection of images depicting multiple disease categories of pomegranate. Experimental results demonstrate the superiority of the hybrid CNN-HBOA system, which achieves a detection accuracy of 99.58%, precision of 100%, recall of 99.71% and F1 score of 99.75% compared to other existing state-of-the-art models. |
| format | Article |
| id | doaj-art-69fa950d4213411b9f9f0f4192bd9cc0 |
| institution | Kabale University |
| issn | 1094-2912 1532-2386 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj-art-69fa950d4213411b9f9f0f4192bd9cc02024-11-18T20:58:53ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862024-12-0127181583710.1080/10942912.2024.2365927Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithmSameera P0Abhay A Deshpande1Department of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, Karnataka, IndiaDepartment of Electronics and Communication Engineering, RV College of Engineering, Bengaluru, Karnataka, IndiaFruit cultivation plays a pivotal role in improvement of the agricultural economy. Pomegranate is a nutritionally rich fruit that is highly valuable because of its excellent antioxidant properties, richness in vitamins and fiber. Pomegranate is affected by diseases, which can severely affect its yield and quality. Timely disease detection is vital for effective disease control in pomegranate orchards. This study presents a novel approach for disease detection in pomegranate and categorization of pomegranate using a hybrid Convolutional Neural Network (CNN) coupled with the Honey Badger Optimization Algorithm (HBOA). The proposed hybrid CNN-HBOA approach leverages the strengths of both CNN and metaheuristic optimization algorithm HBOA. The CNN model was designed to extract high-level features from images of pomegranate affected by various diseases, and HBOA was employed to fine-tune the CNN parameters to improve the accuracy and robustness of the model. Initially, the quality of the images was enhanced using a contrast enhancement technique, and the images were segmented using the Detectron2 algorithm. Finally, the extracted features were fed as input to the disease detection stage wherein the CNN classifies the disease. Furthermore, the classification accuracy of the CNN model was enhanced using the HBOA algorithm. The performance of the hybrid CNN-HBOA algorithm was evaluated using a vast collection of images depicting multiple disease categories of pomegranate. Experimental results demonstrate the superiority of the hybrid CNN-HBOA system, which achieves a detection accuracy of 99.58%, precision of 100%, recall of 99.71% and F1 score of 99.75% compared to other existing state-of-the-art models.https://www.tandfonline.com/doi/10.1080/10942912.2024.2365927Convolutional neural networkhoney badger optimization algorithmpomegranate fruitconfusion matrixdisease classification |
| spellingShingle | Sameera P Abhay A Deshpande Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm International Journal of Food Properties Convolutional neural network honey badger optimization algorithm pomegranate fruit confusion matrix disease classification |
| title | Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| title_full | Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| title_fullStr | Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| title_full_unstemmed | Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| title_short | Disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| title_sort | disease detection and classification in pomegranate fruit using hybrid convolutional neural network with honey badger optimization algorithm |
| topic | Convolutional neural network honey badger optimization algorithm pomegranate fruit confusion matrix disease classification |
| url | https://www.tandfonline.com/doi/10.1080/10942912.2024.2365927 |
| work_keys_str_mv | AT sameerap diseasedetectionandclassificationinpomegranatefruitusinghybridconvolutionalneuralnetworkwithhoneybadgeroptimizationalgorithm AT abhayadeshpande diseasedetectionandclassificationinpomegranatefruitusinghybridconvolutionalneuralnetworkwithhoneybadgeroptimizationalgorithm |