Toward autonomous agriculture: Integrating computer vision and large language models for intelligent weed management

Weed diagnosis and management in agricultural fields still rely heavily on manual observation and expert knowledge, resulting in limited efficiency, accessibility, and decision interactivity. Although computer vision enables automated weed detection and large language models (LLMs) provide new possibilities for intelligent decision support, an integrated framework that connects visual perception with management reasoning is still lacking. To address this challenge, this study proposes an integrated framework for intelligent weed diagnosis and management that combines a weed detection model, an LLM-based decision module, and a user-oriented diagnosis system. A detection model named Mamba-Weed is developed based on the Mamba object detection architecture, incorporating a Lightweight Adaptive Extraction (LAE) module and a Semantics and Detail Infusion (SDI) module to enhance discriminative feature learning and multi-scale feature fusion. An intelligent weed management decision module is then constructed using the DeepSeek LLM, together with Retrieval-Augmented Generation (RAG) and structured prompt engineering to generate domain-specific management recommendations. Experiments on the Weed25 dataset show that Mamba-Weed achieves 92.10% precision, 89.50% recall, and 93.80% mAP@50, demonstrating strong detection performance in complex field environments. The proposed framework establishes a unified pipeline from visual weed identification to intelligent decision-making, providing a practical solution for real-time field weed monitoring and scalable precision weed management.