文献类型: 外文期刊
作者: Cheng, Tao 1 ; Wang, Zhaoming 2 ; Zhao, Chunjiang 5 ; Zhang, Dongyan 1 ; Zhang, Gan 2 ; Wang, Tianyi 3 ; Ren, Weibo 4 ; Yuan, Feng 2 ; Liu, Yaling 2 ;
作者机构: 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Peoples R China
2.Natl Ctr Pratacultural Technol Innovat under prepa, Hohhot 010000, Peoples R China
3.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
4.Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
关键词: Forage; High-throughput phenotyping; Precision identification; Sensors; Artificial intelligence; Efficient breeding
期刊名称:ARTIFICIAL INTELLIGENCE IN AGRICULTURE ( 影响因子:12.4; 五年影响因子:12.7 )
ISSN: 2097-2113
年卷期: 2025 年 15 卷 1 期
页码:
收录情况: SCI
摘要: High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- 相关文献
作者其他论文 更多>>
-
Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm
作者:Tang, Boyi;Zhao, Chunjiang;Tang, Boyi;Zhou, Jingping;Pan, Yuchun;Qu, Xuzhou;Cui, Yanglin;Liu, Chang;Li, Xuguang;Zhao, Chunjiang;Gu, Xiaohe;Li, Xuguang
关键词:Object detection; Maize seedlings; UAV RGB images; YOLOv5; Attention mechanism
-
Boosting Cost-Efficiency in Robotics: A Distributed Computing Approach for Harvesting Robots
作者:Xie, Feng;Xie, Feng;Li, Tao;Feng, Qingchun;Li, Tao;Feng, Qingchun;Chen, Liping;Zhao, Chunjiang;Zhao, Hui
关键词:5G network; computation allocation; edge computing; harvesting robot; visual system
-
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering
作者:Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao
关键词:tassel; 3D phenotyping; TreeQSM; genotyping; clustering
-
Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning
作者:Feng, Haikuan;Fan, Yiguang;Ma, Yanpeng;Liu, Yang;Chen, Riqiang;Bian, Mingbo;Fan, Jiejie;Yang, Guijun;Zhao, Chunjiang;Feng, Haikuan;Zhao, Chunjiang;Yue, Jibo;Fu, Yuanyuan;Leng, Mengdie;Jin, Xiuliang;Zhao, Yu
关键词:Potato; Deep learning; Radiative transfer model; Transfer learning; Leaf protein content
-
Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design
作者:Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhao, Yanxin
关键词:Crop breeding; Next-generation artificial intelligence; Multiomics big data; Intelligent design breeding
-
Water phase distribution and its dependence on internal structure in soaking maize kernels: a study using low-field nuclear magnetic resonance and X-ray micro-computed tomography
作者:Wang, Baiyan;Zhao, Chunjiang;Wang, Baiyan;Gu, Shenghao;Wang, Juan;Wang, Guangtao;Guo, Xinyu;Zhao, Chunjiang
关键词:phenotyping; hydration; water absorption; seed emergence; kernel moisture
-
Achieving optimal cutting results for melon rootstock: Precision linear cutting and parameter simulation optimization
作者:Chen, Shan;Feng, Qingchun;Li, Tao;Jiang, Kai;Zhao, Chunjiang;Chen, Shan;Feng, Qingchun;Kan, Za;Meng, Hewei;Zhao, Chunjiang;Zhang, Qian;Jia, Zhiwei;Zhao, Chunjiang;Li, Tao;Jiang, Kai
关键词:Grafting robot; Rootstock cutting; EDEM; Cutting model; Response surface test; Parameter optimization



