Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement
Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesia...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 31259 - 20 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group UK
25.08.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification. |
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| AbstractList | Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification.Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification. Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification. Abstract Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification. |
| ArticleNumber | 31259 |
| Author | Singh, Sunil K. Kumar, Sudhakar Mahfoudh, Saoucene Jain, Achin Dubey, Arun Kumar Jain, Vanita Arya, Varsha Gupta, Brij B. Panwar, Arvind Hsu, Ching-Hsien Pan, Vincent Shin-Hung Althaqafi, Turki A. Alhalabi, Wadee |
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| Keywords | CNN Bayesian optimization Ensemble learning Potato blight detection Optimizer |
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| Snippet | Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we... Abstract Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy,... |
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| SubjectTerms | 639/166 639/4077 Agricultural production Algorithms Artificial intelligence Automation Bayes Theorem Bayesian analysis Bayesian optimization Classification CNN Deep Learning Disease prevention Ensemble learning Fuzzy Logic Humanities and Social Sciences Image Processing, Computer-Assisted - methods Leaf blight Mathematical models multidisciplinary Neural networks Neural Networks, Computer Optimizer Plant diseases Plant Diseases - microbiology Plant Leaves - microbiology Potato blight detection Potatoes Science Science (multidisciplinary) Solanum tuberosum - microbiology Vegetables |
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| Title | Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement |
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