High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges

被引:0
作者
Cheng, Tao [1 ,2 ]
Wang, Zhaoming [2 ]
Zhao, Chunjiang [5 ]
Zhang, Dongyan [1 ,2 ]
Zhang, Gan [2 ]
Wang, Tianyi [3 ]
Ren, Weibo [4 ]
Yuan, Feng [2 ]
Liu, Yaling [2 ]
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
来源
ARTIFICIAL INTELLIGENCE IN AGRICULTURE | 2025年 / 15卷 / 01期
关键词
Forage; High-throughput phenotyping; Precision identification; Sensors; Artificial intelligence; Efficient breeding; CHLOROPHYLL-A FLUORESCENCE; ALFALFA; YIELD; GRASS; STRESS; PARAMETERS; DROUGHT; QUALITY; SALINE; LIGHT;
D O I
10.1016/j.aiia.2025.01.003
中图分类号
S [农业科学];
学科分类号
09 ;
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/).
引用
收藏
页码:98 / 115
页数:18
相关论文
共 154 条
  • [1] Adhinata F.D., Wahyono, Sumiharto R., A comprehensive survey on weed and crop classification using machine learning and deep learning, Artif. Intell. Agric., 13, pp. 45-63, (2024)
  • [2] Adjorlolo C., Mutanga O., Cho M.A., Estimation of canopy nitrogen concentration across C3 and C4 grasslands using WorldView-2 multispectral data, IEEE J. Select. Topics Appl. Earth Observ. Remote Sens., 7, 11, pp. 4385-4392, (2014)
  • [3] Arend D., Psaroudakis D., Memon J.A., Rey-Mazon E., Schuler D., Szymanski J.J., Et al., From data to knowledge – big data needs stewardship, a plant phenomics perspective, Plant J., 111, 2, pp. 335-347, (2022)
  • [4] Atieno J., Li Y., Langridge P., Dowling K., Brien C., Berger B., Et al., Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping, Sci. Rep., 7, 1, (2017)
  • [5] Azadbakht M., Ashourloo D., Aghighi H., Homayouni S., Shahrabi H.S., Matkan A., Radiom S., Alfalfa yield estimation based on time series of Landsat 8 and PROBA-V images: an investigation of machine learning techniques and spectral-temporal features, Remote Sens. Appl., 25, (2022)
  • [6] Baker N.R., Chlorophyll fluorescence: a probe of photosynthesis in vivo, Annu. Rev. Plant Biol., 59, 1, pp. 89-113, (2008)
  • [7] Ball K.R., Power S.A., Brien C., Woodin S., Jewell N., Berger B., Pendall E., High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture, PLoS One, 15, 10, (2020)
  • [8] Banan D., Paul R.E., Feldman M.J., Holmes M.W., Schlake H., Baxter I., Et al., High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field, Plant Direct, 2, 2, (2018)
  • [9] Bao W., Liu W., Yang X., Hu G., Zhang D., Zhou X., Adaptively spatial feature fusion network: an improved UAV detection method for wheat scab, Precis. Agric., 24, 3, pp. 1154-1180, (2023)
  • [10] Barbedo J., A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses, Drones, 3, 2, (2019)