Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism
Transforming Medicinal Plant Health with Advanced AI
This study addresses limitations in existing approaches by proposing a deep learning-based framework for classifying medicinal plant leaf diseases. It involves image preprocessing (filtering, CLAHE), adaptive thresholding for segmentation, and deep feature extraction using a customized Multi-Scale VGG16. These features are then optimized and selected using a novel Hybridized Zebra with Krill Herd Optimization (HZKHO) algorithm. The selected features are fed into an Attention-based Dilated Adaptive DenseNet (A-DADensenet) model for classification. The model achieved an impressive classification accuracy of 90.69%, demonstrating its effectiveness for real-world agricultural applications by integrating deep learning with a hybrid optimization technique.
Our AI-powered framework delivers significant improvements in accuracy and efficiency for medicinal plant disease detection.
90.69%
Classification Accuracy
Early-stage detection improved
Disease Identification Rate
Enhanced
Feature Optimization Efficiency
Deep Analysis & Enterprise Applications
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The core of this research is an advanced deep learning framework utilizing a customized Multi-Scale VGG16 for deep feature extraction and an Attention-based Dilated Adaptive DenseNet (A-DADensenet) for classification. This approach is designed to capture complex and variable patterns in leaf disease images, addressing the limitations of traditional methods that struggle with high-dimensional datasets and manual feature extraction.
A novel Hybridized Zebra with Krill Herd Optimization (HZKHO) algorithm plays a crucial role in optimizing feature weights and selecting the most relevant features. This hybrid technique enhances the model's classification performance by optimizing hyperparameters like activation function, hidden neuron count, and epochs in the DenseNet model, leading to better convergence rates and avoiding local optima.
The framework begins with image preprocessing, including Median filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE) for visual quality enhancement. An adaptive thresholding mechanism is then employed for precise leaf segmentation, which is essential for accurate disease recognition and subsequent feature extraction.
90.69%
Overall Classification Accuracy
Medicinal Plant Leaf Disease Classification Workflow
Medicinal Plant Leaf Images Collection
→
Image Preprocessing (Filtering & CLAHE)
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Adaptive Thresholding (Segmentation)
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Multi-Scale VGG16 (Deep Feature Extraction)
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Hybridized Zebra with Krill Herd Optimization (Feature Selection & Weighting)
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Attention-based Dilated Adaptive DenseNet (Disease Classification)
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Accurate Disease Identification
| Model | Accuracy | Key Advantages |
|---|---|---|
| Proposed HZKHO-A-DADensenet | 90.69% |
|
| Hybrid Deep Learning Methods | 98.69% (cited) |
|
| Deep CNNs | 99.64% (cited) |
|
| Machine Learning Models | 99.01% (cited) |
|
Impact on Agricultural Development
Early-stage detection of plant diseases is critical for maximizing plant growth and contributing to national agricultural development. This framework provides an automated, precise method to identify diseases, reducing manual effort and time spent by farmers. By ensuring healthier medicinal plant yields, it supports sustainable agriculture and economic growth. The 90.69% accuracy ensures reliable real-world application, directly impacting farmers' ability to make timely, preventative measures. For example, in regions prone to 'Cutting Weevil' or 'Die Back' diseases, this system allows for immediate intervention, preventing widespread crop loss and securing medicinal plant production.
Advanced ROI Calculator
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Your AI Implementation Roadmap
A clear, phased approach to integrating advanced AI for plant disease classification into your operations.
Phase 1: Data Acquisition & Preprocessing
Collect and prepare medicinal plant leaf images, applying filtering and CLAHE. Establish adaptive thresholding for precise segmentation. (~2-4 weeks)
Phase 2: Feature Engineering & Optimization
Extract deep features using Multi-Scale VGG16. Implement HZKHO for weighted fused feature selection and optimization. (~3-6 weeks)
Phase 3: Model Development & Training
Develop and train the Attention-based Dilated Adaptive DenseNet. Optimize model parameters using HZKHO for peak classification performance. (~4-8 weeks)
Phase 4: Validation & Deployment
Rigorously validate the model's accuracy, sensitivity, and specificity. Deploy the solution for real-time plant disease identification in agricultural settings. (~2-4 weeks)
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