Applying Convolutional Neural Networks for Early Detection of Diseases in Sesame Leaf
Description
The Sesame Leaf Disease Dataset consists of 3,540 high-quality images, collected from sesame cultivation fields in Pabna District, Bangladesh. Images were captured under natural field conditions using high-resolution smartphone cameras to ensure clarity of disease symptoms. The dataset is organized into four major classes of sesame leaves. Data Collection Details: Captured Using: 1. Realme 8 (64 MP, f/1.79 aperture) 2. Redmi Note 12 Pro (50MP, f/1.79 aperture) Data Source Location: 1. Pabna District, Bangladesh (Latitude: 24.006355, Longitude: 89.237202) Number of Images (Original 3,540): 1. Healthy: 1,120 2. Insect Damage: 880 3. Leaf Spot: 790 4. Powdery Mildew: 750 Sesame Leaf Dataset Distribution 1. Healthy Leaf – Original Images: 1,335, Augmented Images: 3,000 2. Leaf Spot Disease – Original Images: 587, Augmented Images: 3,000 3. Yellowing Leaf Syndrome – Original Images: 894, Augmented Images: 3,000 4. Insect Leaf Damage – Original Images: 724, Augmented Images: 3,000 Total: Original = 3,540, Augmented = 12,000 Key Applications of the Sesame Leaf Disease Dataset Early Disease Detection: Leaf images help train AI models to detect sesame diseases at an early stage, reducing crop loss. Precision Agriculture: Farmers can use mobile or IoT-based apps powered by trained models to get instant disease diagnosis in the field. Decision Support Systems: Assists agricultural experts and policymakers in monitoring disease spread and planning effective management strategies. Smart Farming Tools: Can be integrated into drones and smart cameras for large-scale sesame field surveillance. Educational and Research Use: Acts as a benchmark dataset for computer vision, deep learning, and plant pathology research. Model Benchmarking: Useful for testing and comparing performance of different CNN architectures and transfer learning models. Scalable to Other Crops: The methodology can be extended to detect diseases in other plants, ensuring wider applications in agriculture.
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Institutions
- Daffodil International University