AI for Pest & Disease Detection: Build an Image Classifier

• Problem framing: farmer takes a picture → app gives “likely disease + advice”.
• Hands-on: mount Google Drive in Colab, list images, show sample images with matplotlib.
• Image quality discussion: lighting, angle, noise and their effect on model performance.
• Mini dataset tour: folder structure (healthy/, diseased/ or 3–4 classes).
• Quick reflection: limitations of AI purely from images.
• Hands-on take-home: Colab notebook 01_pest_image_dataset_exploration.ipynb + folder with labelled training images.

• Concept input: what is transfer learning and why it helps with small datasets.
• Hands-on: load a pre-trained model (e.g., MobileNet/ResNet) and freeze base layers.
• Model head: add a small dense classification layer for pest/disease classes.
• Training lab: train for a few epochs and view accuracy/loss curves.
• Hands-on quick test: upload a new leaf image and see the predicted class.
• Hands-on take-home: Colab notebook 02_pest_classifier_training.ipynb + first working “AI diagnosis” model (even if rough).

• Discussion: error, bias & impact – misclassification → wrong spraying advice and farmer risk.
• Hands-on: add a prediction report cell (predicted class, confidence, advisory template text).
• Hands-on design exercise: sketch an app/WhatsApp bot flow using this model.
• Human in the loop: integrate agronomist/extension worker validation into the flow.
• Closing: how to extend – more classes, multilingual advisory, integration with helplines.
• Hands-on take-home: Colab notebook 03_pest_classifier_reporting_and_flow.ipynb + app-flow diagram for project documentation.

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