Optimizing Wheat Rust Disease Detection with Efficient Net
DOI:
https://doi.org/10.47392/IRJAEH.2025.0267Keywords:
disease detection, computer vision, Vision Transformer, CNN, EfficientNetV2, deep learning, Wheat leaf diseaseAbstract
Wheat rust is one of the most destructive crop diseases, significantly impacting global wheat production. Traditional methods of disease detection are often time-consuming, labor-intensive, and lack the precision required for early intervention. This paper proposes a deep learning-based approach for optimizing wheat rust disease detection using the EfficientNetV2 model. EfficientNetV2 is a powerful convolutional neural network architecture known for its improved accuracy, faster training times, and computational efficiency. The model is trained on a large dataset of wheat leaf images to learn and classify patterns associated with rust infections. By leveraging its advanced feature extraction capabilities, EfficientNetV2 effectively distinguishes between healthy and infected leaves with high precision. The results demonstrate a notable improvement in detection accuracy compared to earlier models, highlighting its potential for real-world agricultural applications. The system also offers a user-friendly interface for image upload and disease prediction, along with suggested remedies for infected plants. This approach contributes to precision farming by enabling timely and accurate detection of wheat rust, ultimately helping reduce crop loss and improve food security.
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