International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Early detection of wheat diseases plays a vital role in minimizing crop losses and improving agricultural productivity. Wheat rust and leaf spot diseases are among the most common threats affecting wheat crops, and manual inspection methods are often time-consuming, subjective, and prone to delays. This project presents an automated image-based system for the detection and classification of wheat rust and leaf spot diseases using computational techniques. The system utilizes publicly available wheat leaf image datasets collected from online repositories and applies a structured workflow that includes image acquisition, preprocessing, feature extraction, classification, and prediction. Image preprocessing techniques such as resizing, noise removal, normalization, and enhancement are employed to improve image quality and highlight disease characteristics. Color and texture-based features are extracted from the preprocessed images to effectively distinguish between healthy and infected leaf samples. A machine learning classifier is trained using the extracted features to classify images into multiple disease categories, including healthy leaves. The trained model is evaluated using standard performance metrics, and experimental results demonstrate reliable classification accuracy, validating the effectiveness of the proposed approach. This system provides a dataset-driven solution that can assist in early disease identification and support timely decision-making for disease management. The proposed framework is scalable and can be extended to detect additional crop diseases in the future, contributing to the development of intelligent agricultural support systems.

Keywords: Wheat leaf analysis, Plant disease detection, Image enhancement, Feature-based classification, Crop health monitoring, image processing, Early disease identification

Downloads: | DOI: 10.17148/IJARCCE.2026.151115