Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Venkat, R.; Sindhu, V.; Prabhu, N.; Venkatrao, Kolli; Manoj Senthil, K.; Bhoopathy, V. Title A name given to the resource Wheat Disease Diagnosis using Transfer Learning on Convolutional Neural Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2025;pp.1805-1810 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIMIA67127.2025.11200732" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIMIA67127.2025.11200732</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022008251?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022008251?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Venkat R., St. Peter's Engineering College (A), Department of Computer Science and Engineering, Telangana, Hyderabad, India; Sindhu V., Christ University, Department of Computer Science, Karnataka, India; Prabhu N., PSGR Krishnammal College for Women, Department of Computer Science With Cognitive Systems, Tamilnadu, Coimbatore, India; Venkatrao K., SRKR Engineering College, Department of ECE, Andhra Pradesh, Bhimavaram, India; Manoj Senthil K., Kongu Engineering College, Department of Electronics and Communication Engineering, Tamilnadu, Erode, India; Bhoopathy V., Sree Rama Engineering College, Department of Computer Science and Engineering, Tirupathi, India Description An account of the resource Wheat disease identification is essential for agricultural output and food security. Traditional diagnostic approaches are slow, ineffective, and need expert assistance, restricting their ability to grow in agriculture. Suggested innovative diagnostic method uses transfer learning on convolutional neural networks (CNNs) to effectively identify and classify wheat leaf diseases. To increase model predictions, high-quality image datasets from open-access platforms are normalised, resized, and augmented. The proposed CNN model performed best with 98.90% accuracy, 98.87% precision, and 98.80% recall. Transfer learning improved model performance by recycling knowledge from pre-trained CNN architectures, reducing training time and enhancing feature extraction. The results show improved precision as well as strength over standard methods and before. This technology helps farmers and agricultural professionals make timely disease management and crop management decisions. To improve disease recognition, future study may use a wider dataset range and other CNN designs. 2025 IEEE. Subject The topic of the resource Accuracy; Feature Extraction; Hyperparameter Tuning; Image Preprocessing; Normalization; Precision; Recall; Resizing; Transfer Learning on Convolutional Neural Networks (CNNs) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155386-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online