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Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement
Achin Jain1, Arun Kumar Dubey1, Vincent Shin-Hung Pan2,3
1Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.
Scientific Reports
|August 25, 2025
Summary
This study introduces a Bayesian Optimized CNN Weighted Ensemble for potato blight detection, achieving 97.94% accuracy. This robust deep learning approach enhances agricultural disease classification and reduces crop losses.
Area of Science:
- Agricultural Science
- Computer Science
- Machine Learning
Background:
- Potato blight causes significant agricultural and economic losses.
- Accurate and early detection of potato blight is crucial for crop management.
Purpose of the Study:
- To develop a highly accurate deep learning model for potato leaf blight detection.
- To optimize Convolutional Neural Network (CNN) models using Bayesian optimization and ensemble learning.
Main Methods:
- Trained multiple CNN architectures (ADAM, SGD, RMSProp, ADAMAX) and evaluated individual performance.
- Applied data augmentation and fuzzy image enhancement to improve feature extraction and mitigate class imbalance.
- Utilized Bayesian optimization to determine optimal weights for a deep ensemble model, exploring 11 combinations.
Main Results:
- The final ensemble model (EDL7: DL1 + DL2 + DL3) achieved a top accuracy of 97.94%.
- The ensemble model demonstrated superior performance over individual CNN models.
- Achieved high precision (0.981), recall (0.983), and F1 score (0.982).
Conclusions:
- Bayesian-optimized ensemble learning significantly improves potato blight detection accuracy.
- The proposed method offers a robust and reliable solution for agricultural disease classification.
- This approach has the potential to minimize crop losses due to potato blight.
