Crop Disease Classification Service Using Transfer Learning

Crop diseases are often identified too late for effective intervention because farmers and field agronomists rarely have quick access to plant pathology expertise. A photograph of a diseased leaf contains enough signal for accurate diagnosis, but turning that signal into a dependable, scalable service is a genuine engineering challenge for agricultural research groups. The intern builds a full diagnostic platform around transfer learning: pre-trained ResNet and MobileNet backbones are fine-tuned in PyTorch on crop disease imagery, with OpenCV and NumPy handling preprocessing and a data augmentation pipeline improving robustness to lighting and orientation. A Streamlit interface lets users upload leaf photos and receive ranked disease predictions alongside explainable AI visualizations that highlight the image regions driving each classification, while a FastAPI service exposes a batch inference endpoint for research workloads and pandas powers summary reporting. The entire system is containerized with Docker so it can be deployed consistently across lab and cloud environments. The project demonstrates end-to-end applied computer vision: adapting pre-trained networks to a specialist domain, communicating model reasoning to non-technical users, and packaging research code as a deployable, reproducible service.