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&rsquo;s robustness and clinical efficacy, whilst the attention mechanisms successfully improved the model&rsquo;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. 
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A Hybrid Approach using ResNet 50 and EfficientNetB0 for Attention-enhanced Deep Learning for Early Detection of Apple Plant Diseases: A Review

Background: The scarcity of publicly available datasets for the detection of cashew leaf disease (DCLD) (Anacardium occidentale L.). This study represents the development and evaluation of cashew leaf disease dataset of three districts. Here, Dataset_1 with 1274 original images and Dataset_2 with 2548 augmented images to increase its diversity. Images were capture using smartphone with natural field conditions, background, various resolutions, angles, Pre-processing and data augmentation techniques were applied to enhance model performance. 
 
Methods: Multiple deep learning models, including DenseNet121, EfficientNetB0, restNet50 and Vision Transformer were trained using transfer learning to classify four cashew leaf condition: Anthracnose, Healthy, Leaves Spot and Powdery Mildew. 
 
Result: DenseNet121 achieved high class wise precision and recall, with overall accuracy between 96% to 97%. EfficientNetB0 improved upon this result, attaining overall accuracy between 96% and 98% with F-1 score up to 97%. RestNet50 and ViTB16 achieved the highest performance, with over accuracy to 99.93% respectively. 
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A Hybrid Approach using ResNet 50 and EfficientNetB0 for Attention-enhanced Deep Learning for Early Detection of Apple Plant Diseases: A Review

Generation and Utilization of an Augmented Cashew Leaf Dataset for Disease Analysis using Transfer Learning