1Department of Computer Applications, B.M.S. College of Engineering (Affiliated to Visvesvaraya Technological University), Bangalore-560 001, Karnataka, India.
Corresponding Author: S. Prasad Ashwini, Department of Computer Applications, B.M.S. College of Engineering (Affiliated to Visvesvaraya Technological University), Bangalore-560 001, Karnataka, India. Email: ashwiniprasadsairam@gmail.com
Early diagnosis of plant diseases constitutes a critical determinant in enhancing agricultural productivity and safeguarding global food security, yet current diagnostic methodologies often lack the precision and efficiency required for widespread agricultural implementation. This research presents a novel hybrid deep learning architecture for apple crop disease classification, leveraging the complementary strengths of ResNet50 and EfficientNetB0 frameworks augmented with sophisticated attention mechanisms. The proposed model integrates spatial, channel and custom attention modules to enhance feature extraction capabilities and enable targeted focus on disease-specific regions within plant imagery, representing a significant advancement over our previous MobileNetV2-based implementation which achieved 97% accuracy. The model was trained on an extensive dataset of apple crop images, incorporating advanced data augmentation techniques to improve generalization across diverse environmental conditions and disease manifestations. The hybrid architecture demonstrated superior performance compared to the baseline MobileNetV2 model, achieving a test accuracy of 98.4% with enhanced F1-scores across all disease categories. Comprehensive evaluation through training-validation loss trajectories, receiver operating characteristic curves and confusion matrix analysis confirmed the model’s robustness and clinical efficacy, whilst the attention mechanisms successfully improved the model’s interpretability by highlighting disease-relevant image regions, thereby enhancing diagnostic confidence. The proposed hybrid deep learning model establishes a new benchmark for automated plant disease detection, offering substantial improvements in accuracy and reliability, with future research directions encompassing real-time field deployment and extension to diverse crop species, potentially revolutionizing precision agriculture practices.
Attention mechanisms, Crop health monitoring, Data augmentation, Deep learning, EfficientNetb0, Hybrid model, Image classification, Plant disease detection, Precision agriculture, ResNet50, Transfer learning
