Multimodal large language model for wheat breeding: a new exploration of smart breeding

Understand

UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data.

• However, the multidisciplinary nature of breeding has brought technical barriers and efficiency challenges to knowledge mining.
• Therefore, it is important to develop a smart breeding goal tool to mine cross-domain multimodal data.
• Based on different pre-trained open-source multimodal large language models (MLLMs) (e.g., Qwen-VL, InternVL, Deepseek-VL), this study used supervised fine-tuning (SFT), retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF) technologies to inject cross-domain knowledge into MLLMs, thereby constructing multiple multimodal large language models for wheat breeding (WBLMs).