Latest Research in Smart Agriculture and AI

Top Research Topics in Smart Agriculture and AI

• Smart Agriculture and AI — 36 papers
• Computer Vision and Pattern Recognition — 6 papers
• Fungal Plant Pathogen Control — 2 papers
• Pomegranate: compositions and health benefits — 1 papers
• Innovative Teaching and Learning Methods — 1 papers
• Spectroscopy and Chemometric Analyses — 1 papers
• Machine Learning and ELM — 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. CoAtNet-DeepMoE: A Convolution-Attention Hybrid with DeepSeek Mixture-of-Experts for Parameter-Efficient Tomato Disease Classification
10. Ensemble Deep Learning for Aloe Vera Leaf Disease Classification
11. Small-Lesion and Boundary-Aware Mask2Former for Pixel-Level Segmentation and Severity Assessment of Cucumber Target Spot Disease
12. LXViT: a hybrid hierarchical CNN-ViT framework for lemon leaf disease classification with multi-method explainable AI integration
13. IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT
14. CBAM-LeafGAN: A selective attention-guided StyleGAN framework for mango leaf disease image synthesis and explainable recognition
15. Hybrid convolutional neural network–random forest framework for automated detection of bell pepper leaf diseases using image analysis
16. Crop Disease Detection and Severity Estimation Using Machine Learning
17. A hybrid ResNet-101 and random forest framework for high-precision multiclass tomato leaf disease classification
18. Plant disease recognition using multi-scale feature progressive enhancement and attentional selective fusion
19. Hybrid explainable DeiT-based framework for plant disease classification and severity estimation
20. AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification