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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2407.19679 |
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| _version_ | 1866914891102683136 |
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| author | Zhu, Hongyan Qin, Shuai Su, Min Lin, Chengzhi Li, Anjie Gao, Junfeng |
| author_facet | Zhu, Hongyan Qin, Shuai Su, Min Lin, Chengzhi Li, Anjie Gao, Junfeng |
| contents | Large models can play important roles in many domains. Agriculture is another key factor affecting the lives of people around the world. It provides food, fabric, and coal for humanity. However, facing many challenges such as pests and diseases, soil degradation, global warming, and food security, how to steadily increase the yield in the agricultural sector is a problem that humans still need to solve. Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks such as pests and diseases, soil quality, and seed quality. It can also help farmers make wise decisions through a variety of information, such as images, text, etc. Herein, we delve into the potential applications of large models in agriculture, from large language model (LLM) and large vision model (LVM) to large vision-language models (LVLM). After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models. Large models have great potential in the field of agriculture. We outline the current applications of agricultural large models, and aims to emphasize the importance of large models in the domain of agriculture. In the end, we envisage a future in which famers use MLLM to accomplish many tasks in agriculture, which can greatly improve agricultural production efficiency and yield. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19679 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing Large Vision and Language Models in Agriculture: A Review Zhu, Hongyan Qin, Shuai Su, Min Lin, Chengzhi Li, Anjie Gao, Junfeng Computer Vision and Pattern Recognition Artificial Intelligence Large models can play important roles in many domains. Agriculture is another key factor affecting the lives of people around the world. It provides food, fabric, and coal for humanity. However, facing many challenges such as pests and diseases, soil degradation, global warming, and food security, how to steadily increase the yield in the agricultural sector is a problem that humans still need to solve. Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks such as pests and diseases, soil quality, and seed quality. It can also help farmers make wise decisions through a variety of information, such as images, text, etc. Herein, we delve into the potential applications of large models in agriculture, from large language model (LLM) and large vision model (LVM) to large vision-language models (LVLM). After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models. Large models have great potential in the field of agriculture. We outline the current applications of agricultural large models, and aims to emphasize the importance of large models in the domain of agriculture. In the end, we envisage a future in which famers use MLLM to accomplish many tasks in agriculture, which can greatly improve agricultural production efficiency and yield. |
| title | Harnessing Large Vision and Language Models in Agriculture: A Review |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.19679 |