Selective Detection of Black Rot in Grapevine Leaves Using Convolutional Neural Networks

Cbam-augmented ResNet for high-accuracy grape leaf disease detection

Mango Leaf Disease Detection Using Transfer Learning with EfficientNet-B0

This study presents a highly optimized, end-to-end deep learning pipeline leveraging transfer learning via the EfficientNet-B0 architecture for multi-class mango leaf disease classification, establishing a robust and computationally efficient baseline for automated precision pathology.

Deep convolutional neural network-based automated identification and classification of mungbean foliar diseases

Early and accurate detection of plant diseases is critical in precision agriculture to improve crop management and yield. Mungbean ( Vigna radiata L.) is highly susceptible to several foliar diseases, including yellow mosaic, powdery mildew, leaf crinkle, and cercospora leaf spot, which cause substantial productivity losses. Despite expanding applications of deep learning in plant disease diagnosis, systematic multi-architecture evaluation for mungbean disease classification under natural field conditions remains limited. This study addresses this gap by evaluating five state-of-the-art deep convolutional neural network (DCNN) architectures on a large-scale, field-acquired mungbean dataset that captures real-world variability across environmental conditions and disease severity levels, distinguishing it from controlled laboratory studies. A total of 5,617 original images across five classes were used. Data augmentation was applied exclusively to the training subset after stratified splitting to prevent data leakage. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets. VGG16, VGG19, ResNet50V2, DenseNet121, and InceptionV3 were evaluated using identical transfer learning and fine-tuning protocols. Model performance was assessed using AUC-ROC, Cohen's kappa coefficient, McNemar's test for pairwise statistical comparisons, five-fold cross-validation, and Grad-CAM-based interpretability. On the independent test set, InceptionV3 achieved the highest accuracy (98.47%) and macro-F1 (98.49%), followed by VGG16 (98.36%) and VGG19 (97.89%). AUC-ROC values exceeded 0.997 for all models, confirming excellent class discrimination. Grad-CAM visualizations further confirmed that model predictions were based on biologically relevant disease symptoms. The findings demonstrate the effectiveness of deep learning for robust disease recognition under realistic field conditions and highlight the potential of AI-based diagnostic tools for crop health monitoring, precision agriculture, and decision-support systems in mungbean production.

Hybrid Deep Learning Architectures for Automated Mango Leaf Disease Detection

India is one of the biggest producers and exporters of mangoes in the world, yet its cultivation is persistently threatened diseases that reduce yield, fruit quality, and orchard longevity. Traditional disease diagnosis is based on agronomists' hand visual inspection, which is a laborious, subjective, and challenging technique to scale across vast plantations. This research provides a hybrid deep learning system that incorporates AlexNet and ResNet-50 for the automated classification of five commercially relevant mango leaf diseases: Bacterial Canker, Anthracnose, Powdery Mildew, Sooty Mould and Healthy foliage. Through a fused, jointly trained classification head, the suggested architecture combines the deep, residual feature hierarchies of ResNet-50 with the shallow, texture-sensitive representations learned by AlexNet, enabling the network to take advantage of complementary visual cues that neither backbone fully captures on its own. The hybrid model was implemented and trained using MATLAB. The trained model achieved a validation accuracy of 99.47%. Comparative analysis against standalone AlexNet, standalone ResNet-50, and other architectures reported in the recent mango plant-disease literature indicates that the hybrid fusion strategy offers a favourable balance of accuracy and convergence stability.

A comparative study of baseline convolutional neural network and ResNet50 for image-based tomato leaf disease classification

A comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification demonstrates that transfer learning can effectively improve classification performance in plant disease recognition tasks.

A novel deep-learning approach for robust identification of plant diseases

This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model.