Leveraging ResNet-50 for Pomegranate Disease Detection : A Convolutional Neural Network

The application of ResNet-50 for pomegranate disease detection using a deep learning-based approach. Adataset comprising multiple disease categories is preprocessed using TensorFlow’s image utilities, anddata augmentation techniques are applied to improve model robustness and mitigate overfitting. Thepretrained ResNet-50 model is employed as a feature extractor, leveraging its deep hierarchicalrepresentations to capture intricate patterns in diseased pomegranate images. A custom fully connectedclassification head is trained on the extracted features, fine-tuned to optimize classification performance.The model is optimized using the Adam optimizer, trained with early stopping and learning ratescheduling to enhance convergence and prevent overfitting. Experimental results demonstrate a highclassification accuracy on the test dataset, validating the effectiveness of transfer learning for accuratepomegranate disease classification. Furthermore, a comparative analysis with other deep learningarchitectures such as VGG16 and InceptionV3 highlights the superior performance of ResNet-50 in termsof accuracy, computational efficiency, and generalization capability. The proposed approach not onlyautomates disease detection but also provides a scalable and reliable solution for real-world agriculturalapplications. The development of AI-driven plant disease detection systems, facilitating early diseasediagnosis, improving crop health monitoring, and supporting precision agriculture to enhance productivityand reduce losses.