Artificial Intelligence In Agriculture

Artificial Intelligence In Agriculture

农业人工智能

  • 1区 中科院分区
  • Q1 JCR分区

期刊简介

《Artificial Intelligence In Agriculture》是由KeAi Communications Co., Ltd.出版社创办的英文国际期刊(ISSN: 2589-7217),该期刊长期致力于农业综合领域的创新研究,主要研究方向为Engineering-Engineering (miscellaneous)。作为SCIE收录期刊(JCR分区 Q1,中科院 1区),本刊采用OA开放获取模式以发表农业综合领域等方向的原创性研究为核心(研究类文章占比84.42%%)。凭借严格的同行评审与高效编辑流程,期刊年载文量精选控制在77篇,确保学术质量与前沿性。成果覆盖Web of Science、Scopus等国际权威数据库,为学者提供推动农林科学领域高水平交流平台。

投稿咨询

投稿提示

Artificial Intelligence In Agriculture审稿周期约为12 Weeks。该刊近年未被列入国际预警名单,年发文量约77篇,录用竞争适中,主题需确保紧密契合农林科学前沿。投稿策略提示:避开学术会议旺季投稿以缩短周期,语言建议专业润色提升可读性。

  • 农林科学 大类学科
  • English 出版语言
  • 是否预警
  • SCIE 期刊收录
  • 77 发文量

中科院分区

《新锐期刊分区表》(2026年3月发布)

Top期刊 综述期刊 大类学科 小类学科
农林科学
1区
AGRICULTURE, MULTIDISCIPLINARY 农业综合 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能
1区 2区

期刊分区表(2025年3月升级版)

Top期刊 综述期刊 大类学科 小类学科
农林科学
1区
AGRICULTURE, MULTIDISCIPLINARY 农业综合 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能
2区 2区

JCR分区

2025-2026年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:AGRICULTURE, MULTIDISCIPLINARY SCIE Q1 1 / 95

99.5

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 8 / 210

96.4

学科:AGRICULTURE, MULTIDISCIPLINARY SCIE Q1 1 / 95

99.47

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 10 / 210

95.48

2023-2024年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:AGRICULTURE, MULTIDISCIPLINARY ESCI Q1 1 / 89

99.4

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q1 19 / 197

90.6

学科:AGRICULTURE, MULTIDISCIPLINARY ESCI Q1 5 / 89

94.94

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q1 22 / 198

89.14

CiteScore(2026年6月最新版)

CiteScore SJR SNIP CiteScore 排名
CiteScore:29.1 SJR:2.496 SNIP:4.328
学科类别 分区 排名 百分位
大类:Agricultural and Biological Sciences 小类:General Agricultural and Biological Sciences Q1 1 / 235

99%

大类:Agricultural and Biological Sciences 小类:Computer Science (miscellaneous) Q1 1 / 179

99%

大类:Agricultural and Biological Sciences 小类:Engineering (miscellaneous) Q1 3 / 300

99%

大类:Agricultural and Biological Sciences 小类:Computer Science Applications Q1 13 / 1022

98%

大类:Agricultural and Biological Sciences 小类:Artificial Intelligence Q1 12 / 570

97%

期刊发文

  • Multimodal remote sensing combination for maize LAI estimation: Stacking model development and phenology-specific feature sensitivity analysi

    Author: Wang, Baoju; Zhu, Junke; Sun, Shuai; Zhang, Lechun; Yan, Yu; Wang, Huizheng; Yang, Weiguang; Lan, Yubin

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 873-888. DOI: 10.1016/j.aiia.2026.03.011

  • Transformer-based cross-view LiDAR-orthomosaic fusion for geo-localization and digital modeling in apple orchard

    Author: Zheng, Yu; Huang, Andong; Li, Anqi; Kim, Do-Hwan; Shen, Yunde; Lee, Kyeong-Hwan

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 804-819. DOI: 10.1016/j.aiia.2026.03.005

  • Empowering Chinese medicinal agriculture through AI-driven technologies: A comprehensive revie

    Author: Zhang, Tingting; Wen, Jin; Tang, Yulu; Tong, Jinpeng; Li, Mingjie; Zhang, Zhongyi; Zhang, Xiaobo; Tian, Xi; Gu, Li

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 889-910. DOI: 10.1016/j.aiia.2026.04.001

  • Optimized modular transfer learning framework integrating PROSAIL and UAV-based hyperspectral reconstruction for cotton canopy water and nitrogen content retrieva

    Author: Yang, Weiguang; Fu, Huaiyuan; Xu, Weicheng; Wu, Jinhao; Liu, Shiyuan; Tan, Jiangtao; Wang, Chaofeng; Chen, Tingting; Lan, Yubin; Zhang, Lei

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 764-787. DOI: 10.1016/j.aiia.2026.03.004

  • Integrating hyperspectral radiation transfer modeling and deep transfer learning to estimate nitrogen density in winter wheat canopie

    Author: Yang, Bo; Zhou, Longfei; Yang, Guijun; Zhang, Guangsheng; Qi, Jingwei; Li, Changchun

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 753-763. DOI: 10.1016/j.aiia.2026.03.003

  • MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimatio

    Author: Wu, Qiang; Ma, Xinming; Wang, Jiao; Du, Yuke; Hou, Dingyi; Cheng, Jinpeng; Cao, Xiaoyu; Wang, Xiaochun; Yang, Hao; Yang, Guijun

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 788-803. DOI: 10.1016/j.aiia.2026.03.007

  • AgriMAPO: A multimodal automatic prompt optimization approach for crop disease classification using large language model

    Author: Shuai, Luyu; Zhu, Linchao; Yang, Yi; Lin, Tao

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 911-925. DOI: 10.1016/j.aiia.2026.04.002

  • PII-CNN-LSTM: A multi-modal deep learning framework integrating novel pollination importance index for predicting optimal apple pollination window

    Author: Manzoor, Shahram Hamza; Zhang, Zhao; Li, Hongwen; Zhang, Qu; Ahmed, Arshed; Yu, Shinning; Abid, Fazeel; Tahir, Naveed; Ye, Dapeng; Abdelhamid, Mahmoud A

    Journal: ARTIFICIAL INTELLIGENCE IN AGRICULTURE. 2026; Vol. 16, Issue 2, pp. 725-752. DOI: 10.1016/j.aiia.2026.03.001