CropHealthNet: Potato Disease Detection Using Depthwise Separable Convolutional Neural Networks

Key Points

• Potato disease detection achieved over 90% accuracy with depthwise separable convolution methods, indicating strong performance.
• Key metrics averaged precision, recall, and F1 score, showcasing the effectiveness of the approach in real-time scenarios.
• Analyzes image classification using depthwise separable convolutional neural networks on a custom dataset of potato plants.
• Technology may enhance diagnosis in agriculture, potentially improving crop yield and reducing losses.