Conceptual

Deep Learning for Plant Leaf Disease Detection

How convolutional neural networks are applied to identify and classify plant leaf diseases from images. Covers building an image dataset and augmenting it, fine-tuning pretrained CNN architectures such as MobileNet through transfer learning, designing custom and multi-scale CNN models, using explainability tools like Grad-CAM to see which leaf regions drive a prediction, and evaluating detection accuracy against state-of-the-art baselines. A worked example of turning a real agricultural problem into a supervised image-classification pipeline.