Ensemble-based feature fusion for accurate plant disease classification using pre-trained models.

ABSTRACT: Agricultural productivity remains seriously threatened by the attacks of plant diseases, even though it is the bedrock of global food security. These diseases, if ignored, can lead to massive crop losses and economic setbacks. Therefore, the development of accurate and efficient plant disease detection systems is essential to preserve plants and promote sustainable agricultural practices. This study examined the potential of ensembled deep learning models that incorporates feature-level fusion to enhance the robustness and accuracy of plant disease detection. Specifically, we utilized pre-trained deep learning architecture (VGG16, Residual Network 50 (ResNet50), and GoogleNet (InceptionV3)) to extract distinctive features representations from plant leaf images. These extracted features wer

SUBMITTER: Apleni T 

PROVIDER: S-EPMC12647784 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature