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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 31259 - 20
Hauptverfasser: Jain, Achin, Dubey, Arun Kumar, Pan, Vincent Shin-Hung, Mahfoudh, Saoucene, Althaqafi, Turki A., Arya, Varsha, Alhalabi, Wadee, Singh, Sunil K., Jain, Vanita, Panwar, Arvind, Kumar, Sudhakar, Hsu, Ching-Hsien, Gupta, Brij B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 25.08.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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
CNN
Bayesian optimization
Ensemble learning
Potato blight detection
Optimizer
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-15940-7