Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change

被引:0
作者
Nguyen, Hoa Thi [1 ]
Khan, Md Arifur Rahman [2 ,3 ]
Nguyen, Thuong Thi [4 ]
Pham, Nhi Thi [2 ]
Nguyen, Thu Thi Bich [5 ]
Anik, Touhidur Rahman [2 ]
Nguyen, Mai Dao [2 ]
Li, Mao [6 ]
Nguyen, Kien Huu [7 ]
Ghosh, Uttam Kumar [3 ]
Tran, Lam-Son Phan [2 ]
Ha, Chien Van [2 ]
机构
[1] Agr Genet Inst, Natl Key Lab Plant Biotechnol, Hanoi 100000, Vietnam
[2] Texas Tech Univ, Inst Genom Crop Abiot Stress Tolerance, Dept Plant & Soil Sci, Lubbock, TX 79409 USA
[3] Bangabandhu Sheikh Mujibur Rahman Agr Univ, Dept Agron, Gazipur 1706, Bangladesh
[4] Vietnam Natl Univ Agr, Hanoi 100000, Vietnam
[5] FPT Univ, Quy Nhon 590000, Vietnam
[6] Donald Danforth Plant Sci Ctr, St Louis, MO 63132 USA
[7] Agr Genet Inst, Dept Genet Engn, Hanoi 100000, Vietnam
来源
PLANTS-BASEL | 2025年 / 14卷 / 06期
关键词
high-throughput phenotyping; hyperspectral imaging; machine learning; plant stress tolerance; unmanned aerial vehicles; LEAF CHLOROPHYLL CONTENT; PLANT-DISEASE; SPECTRAL REFLECTANCE; VEGETATION INDEX; STRESS; MAIZE; ASSOCIATION; PHENOMICS; WHEAT; IDENTIFICATION;
D O I
10.3390/plants14060907
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate.
引用
收藏
页数:16
相关论文
共 50 条
[11]   High-throughput Phenotyping and Genomic Selection: The Frontiers of Crop Breeding Converge [J].
Cabrera-Bosquet, Llorenc ;
Crossa, Jose ;
von Zitzewitz, Jarislav ;
Dolors Serret, Maria ;
Luis Araus, Jose .
JOURNAL OF INTEGRATIVE PLANT BIOLOGY, 2012, 54 (05) :312-320
[12]   High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat [J].
Singh, Daljit ;
Wang, Xu ;
Kumar, Uttam ;
Gao, Liangliang ;
Noor, Muhammad ;
Imtiaz, Muhammad ;
Singh, Ravi P. ;
Poland, Jesse .
FRONTIERS IN PLANT SCIENCE, 2019, 10
[13]   High-throughput Phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge [J].
Lloren CabreraBosquet ;
Jos Crossa ;
Jarislav von Zitzewitz ;
Mara Dolors Serret ;
Jos Luis Araus .
JournalofIntegrativePlantBiology, 2012, 54 (05) :312-320
[14]   High-throughput proximal ground crop phenotyping systems - A comprehensive review [J].
Rui, Z. ;
Zhang, Z. ;
Zhang, M. ;
Azizi, A. ;
Igathinathane, C. ;
Cen, H. ;
Vougioukas, S. ;
Li, H. ;
Zhang, J. ;
Jiang, Y. ;
Jiao, X. ;
Wang, M. ;
Ampatzidis, Y. ;
Oladele, O. I. ;
Ghasemi-Varnamkhasti, M. ;
Radi, Radi .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 224
[15]   Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants [J].
Bhat, Javaid Akhter ;
Deshmukh, Rupesh ;
Zhao, Tuanjie ;
Patil, Gunvant ;
Deokar, Amit ;
Shinde, Suhas ;
Chaudhary, Juhi .
JOURNAL OF BIOTECHNOLOGY, 2020, 324 :248-260
[16]   High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms [J].
Jin, Xiuliang ;
Zarco-Tejada, Pablo J. ;
Schmidhalter, Urs ;
Reynolds, Matthew P. ;
Hawkesford, Malcolm J. ;
Varshney, Rajeev K. ;
Yang, Tao ;
Nie, Chengwei ;
Li, Zhenhai ;
Ming, Bo ;
Xiao, Yonggui ;
Xie, Yongdun ;
Li, Shaokun .
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2021, 9 (01) :200-231
[17]   Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping [J].
Guo, Qinghua ;
Wu, Fangfang ;
Pang, Shuxin ;
Zhao, Xiaoqian ;
Chen, Linhai ;
Liu, Jin ;
Xue, Baolin ;
Xu, Guangcai ;
Li, Le ;
Jing, Haichun ;
Chu, Chengcai .
SCIENCE CHINA-LIFE SCIENCES, 2018, 61 (03) :328-339
[18]   Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry [J].
Gouache, David ;
Beauchene, Katia ;
Mini, Agathe ;
Fournier, Antoine ;
de Solan, Benoit ;
Baret, Fred ;
Comar, Alexis .
AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING, 2016, 9866
[19]   Editorial: Harnessing genebanks: High-throughput phenotyping and genotyping of crop wild relatives and landraces [J].
Cortes, Andres J. ;
Barnaby, Jinyoung Y. .
FRONTIERS IN PLANT SCIENCE, 2023, 14
[20]   Editorial: Enriching genomic breeding with environmental covariates, crop models, and high-throughput phenotyping [J].
Xavier, Alencar ;
Rainey, Katy M. ;
Robbins, Kelly R. .
FRONTIERS IN GENETICS, 2024, 15