Cross-Modal Apple Disease Recognition: Enhancing Apple Leaf Disease Detection Through Vision–Language Feature Alignment

Global-Local Feature-Based Rice Leaf Disease Classification Using Two-Stream Deep Neural Network Feature Fusion

The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications and effectively integrates global semantic information with local disease-specific features for improved classification performance.

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

Grape Leaf Disease Detection through Hierarchical Features, Mask R-Convolutional Neural Network Segmentation, and Self-Attention Networks

Automated grape leaf disease detection is essential for precision agriculture, enabling early diagnosis and effective disease management. This paper presents a novel deep learning-based framework to enhance the accuracy and robustness of grape disease detection by integrating advanced image pre-processing, segmentation...

Hybrid CNN and LLM for Image-Based Classification of Plant Leaf Diseases

A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic r...

AI-driven agricultural productivity: detecting potato leaf diseases with multi-scale feature extraction

A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.

AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring

AgriFusionNet is discussed, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification and facilitates the co-learning of visual, semantic, and contextual environmental representations.