Automated Mango Leaf Disease Identification with Improved Grey Wolf Optimizer in Hybrid MaxViT-Swin Transformer Model

Mango Fruit Disease Classification Using Transformer-Based Feature Learning and Optimized Ensemble Modeling

The results show that the proposed approach enables accurate, robust, and explainable disease detection, making it a promising tool for precision agriculture and early diagnosis in mango orchards.

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.

HYBRID DEEP LEARNING WITH PCA-OPTIMIZED FEATURE SELECTION IN EARLY-STAGE PAPAYA LEAF DISEASE DIAGNOSIS

Early detection of leaf diseases is essential to maintain crop health and improve agricultural yield. This study proposes an advanced system that uses artificial intelligence (AI) and principal component analysis (PCA) for efficient feature selection in papaya leaf disease classification. The system uses a combination of deep learning models, including VGGNet, ResNet and GoogLeNet, to extract critical features from a comprehensive dataset of healthy and diseased papaya leaf images. PCA is applied to reduce the dimensionality of the extracted features and select the most relevant features for accurate classification. The selected features are classified using linear discriminant analysis, resulting in an impressive accuracy of 96.57%. This high accuracy demonstrates the effectiveness of the proposed method in diagnosing papaya leaf diseases. In addition, the open-source nature of the system encourages reproducibility and further research, providing a valuable tool for the agricultural community. By providing a reliable and efficient solution for early disease detection, this approach will assist farmers in taking timely action, ultimately contributing to the sustainability and productivity of papaya cultivation. The integration of AI and PCA in this system marks a significant advancement in the field, highlighting its potential for wider application in agricultural disease management.

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques

Deep learning based groundnut and paddy leaf disease classification using dual attention network.

Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.

Deep Learning-based Bean Leaf Disease Classification: A Comparison of ResNet50 and VGG19

Background: Legumes, such as beans, are important in worldwide agriculture because of their nutritional value and soil-enriching qualities. However, bean crops are susceptible to diseases such as angular leaf spot and rust, which may reduce production and quality. Disease identification that is both effective and timely is essential to crop health and output. Traditional diagnostic approaches are often labor-intensive and susceptible to inaccuracy. Recent advances in deep learning (DL) provide interesting possibilities for automating disease categorization, possibly improving accuracy and efficiency. Methods: This study evaluates and compares the performance of two deep learning architectures, ResNet50 and VGG19, for the classification of bean leaf diseases. The dataset, sourced from Kaggle, comprises 1295 images categorized into three classes: Angular Leaf Spot, Rust and Healthy. Both systems relied on pre-trained ImageNet weights, with adjustments customized to the classification objective. The models were trained for 25 epochs and their performance was assessed based on overall accuracy. Result: The performance of the models is evaluated in terms of the confusion matrix, classification report and ROC(AUC) curves. The ResNet50 model achieved an overall accuracy of 93.75%, while the VGG19 model attained an accuracy of 91.41%. The findings indicate that ResNet50 performs better than VGG19 in terms of classification accuracy. This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.