ArticleFrontiers in plant science2025
Cotton pest and disease diagnosis via YOLOv11-based deep learning and knowledge graphs: a real-time voice-enabled edge solution.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- YOLOv12n-RMB: An Improved Dual Target Recognition Network for Seedling Maize and Field Weeds in Cold Regions.Plants (Basel, Switzerland) · 2026Article
- A multi-source fusion and feedback-optimized intelligent agent for crop disease and pest diagnosis and treatment.Frontiers in plant science · 2026Article
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3 authors.
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Abstract
Introduction: High labor costs, limited expert availability, and slow response hinder cotton pest and disease management. We propose a real-time, voice-enabled edge solution that integrates deep learning-based detection with a domain knowledge graph to deliver accessible, field-ready decision support. Methods: We construct a cotton pest-disease knowledge graph with over 3,000 triples spanning seven major categories by fusing expert-curated and web-sourced knowledge. For image recognition, we develop an enhanced YOLOv11 detector compressed via LAMP pruning and a teacher-assistant-student distillation strategy for lightweight, high-performance deployment on Jetson Xavier NX. Detected objects are semantically aligned to graph entities to generate context-aware recommendations, which are delivered through Bluetooth voice feedback for hands-free use. Results: The optimized model has 0.3M parameters and achieves Discussion: Coupling a compact detector with a structured knowledge graph and voice interaction reduces dependence on expert labor and speeds response in non-expert settings, demonstrating a practical pathway to scalable, intelligent cotton pest and disease management at the edge.
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