Summary of Harnessing Large Vision and Language Models in Agriculture: a Review, by Hongyan Zhu et al.
Harnessing Large Vision and Language Models in Agriculture: A Review
by Hongyan Zhu, Shuai Qin, Min Su, Chengzhi Lin, Anjie Li, Junfeng Gao
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large models have significant potential in various domains, including agriculture. The agricultural sector faces numerous challenges such as pests, diseases, soil degradation, global warming, and food security, making it crucial to increase yields efficiently. Large models can aid farmers by detecting tasks like pest and disease detection, soil quality assessment, and seed quality evaluation. They can also provide valuable decision-making insights through various data types, including images and text. This paper explores the applications of large models in agriculture, encompassing large language models (LLMs), large vision models (LVMs), and large vision-language models (LVLMs). Multimodal large language models (MLLMs) can solve problems like agricultural image processing, question answering systems, and machine automation. Large models have vast potential in agriculture, with current applications including tasks such as crop monitoring, disease detection, and yield prediction. The paper emphasizes the importance of large models in agriculture, envisioning a future where farmers utilize MLLMs to enhance production efficiency and yields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large models can help solve problems in agriculture, making it easier for farmers to produce more food. Right now, farming is facing many challenges like pests, diseases, and climate change. Large models can detect things like pests and diseases, check soil quality, and evaluate seed quality. They can also give farmers valuable information by processing images and text. This paper looks at how large models can be used in agriculture to help solve these problems. |
Keywords
» Artificial intelligence » Question answering
