Foundation Models in Agriculture: A Comprehensive Review
This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including general...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/8/847 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183743057166336 |
|---|---|
| author | Shuolei Yin Yejing Xi Xun Zhang Chengnuo Sun Qirong Mao |
| author_facet | Shuolei Yin Yejing Xi Xun Zhang Chengnuo Sun Qirong Mao |
| author_sort | Shuolei Yin |
| collection | DOAJ |
| description | This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including general FM training processes, application trends and challenges, before focusing on Agricultural Foundation Models (AFMs). The paper examines the diversity and applications of AFMs in areas like crop classification, pest detection, and crop image segmentation, and delves into specific use cases such as agricultural knowledge question-answering, image and video analysis, decision support, and robotics. Furthermore, it discusses the challenges faced by AFMs, including data acquisition, training efficiency, data shift, and practical application challenges. Finally, the paper discusses future development directions for AFMs, emphasizing multimodal applications, integrating AFMs across the agricultural and food sectors, and intelligent decision-making systems, ultimately aiming to promote the digitalization and intelligent transformation of agriculture. |
| format | Article |
| id | doaj-art-db4ffd2ab1cf4191b16f47f1632d6d1d |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-db4ffd2ab1cf4191b16f47f1632d6d1d2025-08-20T02:17:14ZengMDPI AGAgriculture2077-04722025-04-0115884710.3390/agriculture15080847Foundation Models in Agriculture: A Comprehensive ReviewShuolei Yin0Yejing Xi1Xun Zhang2Chengnuo Sun3Qirong Mao4School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, ChinaThis paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including general FM training processes, application trends and challenges, before focusing on Agricultural Foundation Models (AFMs). The paper examines the diversity and applications of AFMs in areas like crop classification, pest detection, and crop image segmentation, and delves into specific use cases such as agricultural knowledge question-answering, image and video analysis, decision support, and robotics. Furthermore, it discusses the challenges faced by AFMs, including data acquisition, training efficiency, data shift, and practical application challenges. Finally, the paper discusses future development directions for AFMs, emphasizing multimodal applications, integrating AFMs across the agricultural and food sectors, and intelligent decision-making systems, ultimately aiming to promote the digitalization and intelligent transformation of agriculture.https://www.mdpi.com/2077-0472/15/8/847foundation modelssmart agriculturelanguage foundation modelsvision foundation modelsmultimodal foundation modelsagriculture foundation models |
| spellingShingle | Shuolei Yin Yejing Xi Xun Zhang Chengnuo Sun Qirong Mao Foundation Models in Agriculture: A Comprehensive Review Agriculture foundation models smart agriculture language foundation models vision foundation models multimodal foundation models agriculture foundation models |
| title | Foundation Models in Agriculture: A Comprehensive Review |
| title_full | Foundation Models in Agriculture: A Comprehensive Review |
| title_fullStr | Foundation Models in Agriculture: A Comprehensive Review |
| title_full_unstemmed | Foundation Models in Agriculture: A Comprehensive Review |
| title_short | Foundation Models in Agriculture: A Comprehensive Review |
| title_sort | foundation models in agriculture a comprehensive review |
| topic | foundation models smart agriculture language foundation models vision foundation models multimodal foundation models agriculture foundation models |
| url | https://www.mdpi.com/2077-0472/15/8/847 |
| work_keys_str_mv | AT shuoleiyin foundationmodelsinagricultureacomprehensivereview AT yejingxi foundationmodelsinagricultureacomprehensivereview AT xunzhang foundationmodelsinagricultureacomprehensivereview AT chengnuosun foundationmodelsinagricultureacomprehensivereview AT qirongmao foundationmodelsinagricultureacomprehensivereview |