Plant Species Identification From Leaf Photos

Added Jun 2025 3 design docs

Farmers, gardeners, and agriculture students constantly encounter plants they cannot name, and traditional identification guides are slow, regional, and nearly impossible to search by appearance. A tool that identifies a species from a single leaf photo effectively puts field botany in anyone's pocket. The intern builds a Python web app where users upload a leaf photo and a machine learning model predicts the plant species. OpenCV drives the vision pipeline, handling background removal, contour detection, and the extraction of shape, vein, and texture features that feed a classifier trained on a labeled leaf dataset. The app returns top predictions with confidence scores and reference images so users can visually confirm the match, and the intern evaluates accuracy honestly across species, lighting conditions, and photo quality, documenting where the model struggles and why. As an introduction to computer vision in agriculture, the project covers the full arc from raw images to a deployed classifier: dataset preparation, the trade-off between engineered features and end-to-end learning, sound evaluation methodology, and packaging inference behind a simple upload interface anyone can use in the field.

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