Edge-Optimized Lightweight Vision Framework for Real-Time Crop Disease Diagnosis on Low-Resource Devices

An edge-optimised lightweight vision framework for real-time crop disease diagnosis on low-resource devices.

An edge-optimised lightweight vision framework for real-time crop disease diagnosis on low-resource devices. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are real-time processing of incoming data, dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses Apache Kafka, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.