gradio_article.md

Description

This project aims to revolutionize paddy cultivation practices prevalent in Asian countries, where vulnerabilities to diseases and pests result in substantial yield losses of up to 70%. Traditionally, expert supervision has been pivotal in managing these issues, but due to limited availability and high costs, a more innovative approach is warranted.

At the core of the project lies the automation of the crucial process of disease identification in paddy crops using state-of-the-art computer vision techniques. This cutting-edge solution draws inspiration from successful applications in diverse domains, where computer vision has showcased remarkable potential.

By leveraging a comprehensive training dataset comprising 10,407 labeled images, encompassing ten distinct classes ranging from various disease categories to normal leaves, the goal is to build a model that can seamlessly replicate the expertise of crop protection professionals.

Notably, the project doesn't stop at imagery alone; it delves deeper by incorporating additional metadata associated with each image, such as the paddy variety and age. The final challenge is to successfully classify users' input images into nine disease categories or, alternatively, identify a healthy, normal leaf.

Examples

The example images provided below are not from the training set. Users can also upload images from their local device or via the phone's camera.