Wheat Disease Detection Web App using Ultralytics YOLOv8

Harchet Singh Majithia

ABSTRACT 

Wheat diseases have a significant impact on global wheat production. These diseases are often not detected on time which causes major crop loss and economic damage. Some of these diseases spread rapidly, often leaving the whole field in ruin. Sometimes farmers mistake the diseases for a different one and implement the wrong cure or take incorrect precautionary measures. All these problems can be solved in one stroke using WheatCheck. This Wheat disease detection Web App is free to use and easily available online to all users. It accurately detects the disease in seconds using an image detection model called Ultralytics YOLOv8 and even provides the user with the causes, preventions, and remedies. The app uses a Kaggle dataset to train the model on common wheat diseases, including rusts, mites and blights. It uses Streamlit to convert the algorithm into an interactive web application. The integration of machine learning (ML) with intuitive web design ensures that the WheatCheck app can be widely adopted across various platforms. This tool holds the potential to revolutionize wheat farming practices and significantly reduce crop losses due to disease.

Wheat Disease Detection Web App using Ultralytics YOLOv8.pdf

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