DenseNet169-Enabled High-Accuracy Automated Detection System for Cotton Leaf Diseases Using Deep Transfer Learning

A Modified EfficientNetB0-based Deep Learning Model for Accurate Detection and Classification of Groundnut Leaf Diseases

A deep learning-based solution to automate disease detection of groundnut leaf conditions that outperformed existing methods such as ResNet50, CNN with progressive resizing, LeafNet and LeafNet and maintained low training and validation loss throughout training.

Corn Leaf Disease Detection and Classification using Deep Learning Models

Corn diseases are a major threat to the food security of the world. However, the existing deep learning models are "heavy" in terms of computation and are not feasible for mobile applications. This paper proposes a model by modifying the existing DenseNet and EfficientNet models for the classification of four different types of corn leaves, namely Common Rust, Cercospora Leaf Spot, Northern Leaf Blight, and Healthy. Using the PlantVillage dataset and the existing data augmentation strategy, the proposed model integrating EfficientNet and DenseNet models has shown a high accuracy in terms of validation. This model has shown the feasibility of the high-precision corn leaf disease detection with minimal memory usage. Experimental results demonstrate that the proposed hybrid EfficientNetB0–DenseNet121 model achieves a classification accuracy of 96.94% with reliable and consistent performance. The model provides an efficient solution for accurate corn leaf disease detection while maintaining low computational complexity. These results highlight its potential for practical deployment in intelligent and precision agriculture applications.

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

Deep Learning-Based Potato Leaf Disease Detection Using Transfer Learning and Explainable AI Techniques

This study improves potato leaf disease detection using a fine-tuned InceptionV3 with data augmentation and dropout, while Grad-CAM visualizations enhance model interpretability, reliability, and practical utility for accurate agricultural disease diagnosis.

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.

Implementation of MobileNetV2-Based Deep Learning for Corn Leaf Disease Detection Using a Web-Based System

The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.