SOYFIB is a computer vision platform engineered to automate the detection and classification of soybean leaf diseases. Using a novel, hybrid deep learning architecture, the platform extracts high-dimensional visual signatures from crop imagery to perform zero-shot disease identification and provides interactive tools for researchers to dynamically expand the diagnosis database.

Key Highlights & Engineering Contributions

Hybrid Feature Extraction Pipeline: Built a deep learning vision pipeline combining a custom CNN with a Swin Transformer architecture to extract 512-dimensional visual disease fingerprints from soybean leaf images.

Dynamic Zero-Shot Detection Backend: Implemented a zero-shot diagnosis system by indexing extracted feature embeddings into a FAISS vector database, enabling ultra-fast vector matching and similarity retrieval.

Custom Dataset Preprocessing: Built an end-to-end data pipeline to handle inputs of 225 X 225 X 3 dimensions and parse YOLO-formatted annotations (.txt), optimizing feature extraction accuracy.

Web Platform & Researcher Portal: Developed a Flask web application allowing users to upload geolocated crop images and view real-time diagnostics, paired with an admin portal for researchers to register new disease vectors dynamically.