An ensemble deep learning approach for walnut disease detection using VGG16 and ResNet50
DOI:
https://doi.org/10.62431/xf2fc990Keywords:
Walnut Disease, Transfer Learning, Deep Learning, Plant DiseaseAbstract
Walnut yield is impacted by walnut diseases, among which walnut anthracnose is a major foliar disease. These diseases, if not managed, can lead to crop and economic loss. Thus, there is a need to develop accurate and effective disease detection systems to promote sustainable agriculture practices. This study presents an ensemble model for walnut anthracnose disease detection that uses deep learning (DL) by implementing a feature-level ensemble of Visual Geometry Group 16 (VGG16) and Residual Network 50 (ResNet50) architectures. In order to use the features learned by the pre-trained models, transfer learning is used, and then fine-tuning is employed so that the model adapts to disease-specific visual patterns. Both networks act as feature extractors and their deep representations are fused through concatenation, followed by a trainable classification layer. The experiments were conducted using a custom walnut disease dataset consisting of 2091 images across two classes—healthy and anthracnose. The dataset was prepared by collecting images from the walnut fields of the Kashmir Region. The proposed model achieves notable results with an accuracy of more than 99%. This demonstrates the capability of feature-fusion ensemble learning in improving detection stability and accuracy. The results demonstrate that the proposed model achieves high accuracy for disease diagnosis, thus making it a good choice for practical deployment in precision agriculture systems.
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