Summary of Multimodal Large Language Model For Wheat Breeding: a New Exploration Of Smart Breeding, by Guofeng Yang et al.
Multimodal large language model for wheat breeding: a new exploration of smart breeding
by Guofeng Yang, Yu Li, Yong He, Zhenjiang Zhou, Lingzhen Ye, Hui Fang, Yiqi Luo, Xuping Feng
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a smart breeding goal tool that leverages multimodal large language models (MLLMs) to integrate cross-domain knowledge in crop breeding. The authors fine-tune pre-trained MLLMs using supervised learning, retrieval-augmented generation, and reinforcement learning from human feedback. They construct multiple multimodal MLLMs for wheat breeding (WBLMs) and evaluate them on a newly created benchmark. The results show that the WBLM constructed using SFT, RAG, and RLHF technologies performs best, outperforming other models in tasks such as wheat yield prediction, phenotyping estimation, environmental stress assessment, target germplasm screening, cultivation technique recommendation, and seed price query. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us develop a smarter way to breed crops using special machines called drones. These drones can collect lots of data about the plants without harming them. However, this data is hard to understand because it’s from different fields like computer science and biology. To fix this, researchers created special language models that can learn from many types of data. They tested these models on wheat breeding tasks and found that one model worked really well at predicting how much wheat would grow. This model also helped with other important tasks like guessing the right kind of plant to use and recommending the best way to care for the plants. |
Keywords
» Artificial intelligence » Rag » Reinforcement learning from human feedback » Retrieval augmented generation » Rlhf » Supervised
