Latest Research in Phytopathological Image Diagnostics

Top Research Topics in Phytopathological Image Diagnostics

• Smart Agriculture and AI — 48 papers
• Computer Vision and Pattern Recognition — 9 papers
• Fungal Plant Pathogen Control — 2 papers
• Spectroscopy and Chemometric Analyses — 2 papers
• Machine Learning and ELM — 2 papers
• Pomegranate: compositions and health benefits — 1 papers
• Coffee research and impacts — 1 papers
• Wheat and Barley Genetics and Pathology — 1 papers
• Wood and Agarwood Research — 1 papers
• Innovative Teaching and Learning Methods — 1 papers

Highest-Cited Papers

1. Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis
2. Multi scale feature fusion method for assessing the degree of damage caused by rice blast disease
3. MMTA-ACDD: Multi-teacher online adaptive learning and multimodal augmentation synergy for robust crop disease detection
4. Lightweight CNN Architectures for Real-Time Crop Disease Detection on Low-End Smartphones for Rural Deployment
5. Contrastive learning with prototype reliability calibration for plant leaf recognition
6. A bilevel-optimized multimodal transformer framework for plant disease classification and severity prediction using adaptive feature fusion
7. A Multi-Modal Generative Model for Tomato Disease Leaves Understanding
8. A field-acquired annotated dataset for robust and generalizable pomegranate disease detection using generative learning
9. Vision Normalizing Flows for the probability-informed detection of banana diseases from in-field images
10. CoAtNet-DeepMoE: A Convolution-Attention Hybrid with DeepSeek Mixture-of-Experts for Parameter-Efficient Tomato Disease Classification
11. Ensemble Deep Learning for Aloe Vera Leaf Disease Classification
12. Small-Lesion and Boundary-Aware Mask2Former for Pixel-Level Segmentation and Severity Assessment of Cucumber Target Spot Disease
13. LXViT: a hybrid hierarchical CNN-ViT framework for lemon leaf disease classification with multi-method explainable AI integration
14. Ensemble Deep Learning for Aloe Vera Leaf Disease Classification
15. IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT
16. CBAM-LeafGAN: A selective attention-guided StyleGAN framework for mango leaf disease image synthesis and explainable recognition
17. Hybrid convolutional neural network–random forest framework for automated detection of bell pepper leaf diseases using image analysis
18. Crop Disease Detection and Severity Estimation Using Machine Learning
19. A hybrid ResNet-101 and random forest framework for high-precision multiclass tomato leaf disease classification
20. Plant disease recognition using multi-scale feature progressive enhancement and attentional selective fusion

Sub-Regions

• Smart Agriculture and AI — 48 papers
• Computer Vision and Pattern Recognition — 10 papers
• Machine Learning and ELM — 7 papers
• Wood and Agarwood Research — 2 papers
• Coffee research and impacts — 2 papers
• Smart Agriculture and AI — 1 papers