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@article{172652,
        author = {Pooja A B and Monisha H H and Miladunnisa J S and Spoorthi A M and Mr. Guruprasad K M and Mr. Vijay R},
        title = {Wheat Plant Leaf Disease Detection And Classification Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {792-797},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172652},
        abstract = {Wheat disease Finding is a critical duty in agriculture to guarantee a good crop output and avoid financial losses. Detecting diseases in wheat is crucial for ensuring good crop yields and preventing financial losses in agriculture. Recently, deep learning techniques have shown promising results in identifying and classifying wheat diseases from images. In this study, we propose a system that uses the VGG19 deep convolution neural network (CNN) architecture to detect wheat diseases. The system works with a dataset of images showing both healthy and diseased wheat plants. These images are pre-processed through resizing, normalization and enhancement. The dataset includes photos of wheat affected by 10 different diseases, such as powdery mildew, yellow rust and leaf rust. Our approach fine-tunes a pre-trained VGG19 model on this dataset. To evaluate its effectiveness, we use metrics like accuracy, precision, recall and F1 score. The results indicate that our model outperforms other state of wheat disease detection algorithms, achieving an impressive 97.65% accuracy on the validation dataset. This method could be practically applied to help farmers quickly and accurately identify wheat diseases, leading to better crop management and reduced losses. Moreover, the approach can potentially be adapted to other crops and diseases, representing a valuable contribution to both agriculture and computer vision fields.
The significance of this exploration lies in its capability to bridge gaps between theoretical advancements and practical operations, especially in the environment of global agricultural challenges. By understanding the methodologies and results of these studies, this paper aims to give a consolidated resource for experimenters, masterminds, and interpreters in this arising sphere.},
        keywords = {Machine learning, Deep learning, Leaf disease, Convolutional neural networks, VGG19.},
        month = {February},
        }