Wheat Disease Diagnosis using Transfer Learning on Convolutional Neural Networks
Accuracy; Feature Extraction; Hyperparameter Tuning; Image Preprocessing; Normalization; Precision; Recall; Resizing; Transfer Learning on Convolutional Neural Networks (CNNs)
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.
Venkat, R.; Sindhu, V.; Prabhu, N.; Venkatrao, Kolli; Manoj Senthil, K.; Bhoopathy, V.
Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2025;pp.1805-1810
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICIMIA67127.2025.11200732 https://www.scopus.com/pages/publications/105022008251?origin=resultslist
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