# Web‐Based Sustainable Detection and Treatment Recommendation System for Wheat Plant Diseases Using Convolutional Neural Networks

## TL;DR

A web-based system using CNNs helps detect wheat plant diseases like rust and recommends treatments, aiding farmers in protecting their crops.

## Contribution

A CNN-based web application for detecting wheat rust diseases and providing treatment recommendations is developed and evaluated.

## Key findings

- The CNN model achieved 96% accuracy in classifying wheat plant diseases.
- The system includes a treatment recommendation module for disease-specific advice.
- The web application is user-friendly and accessible to farmers for practical crop management.

## Abstract

Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web‐based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user‐friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.

A comprehensive dataset of wheat crop images, including healthy plants and samples affected by YR and BR diseases, was developed from public sources. A Convolutional Neural Network (CNN) model was trained and optimized for accurate detection and classification of wheat diseases. The trained model was integrated into a user‐friendly web application, enabling farmers and agricultural stakeholders to quickly identify wheat diseases and receive effective treatment recommendations for crop protection and management.