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
[21]   High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils [J].
Huang, Liping ;
Zhang, Yujing ;
Guo, Jieru ;
Peng, Qianlan ;
Zhou, Zhaoyang ;
Duan, Xiaosong ;
Tanveer, Mohsin ;
Guo, Yongjun .
FRONTIERS IN PLANT SCIENCE, 2023, 14
[22]   Advancing crop improvement through GWAS and beyond in mung bean [J].
Ahmed, Syed Riaz ;
Asghar, Muhammad Jawad ;
Hameed, Amjad ;
Ghaffar, Maria ;
Shahid, Muhammad .
FRONTIERS IN PLANT SCIENCE, 2024, 15
[23]   The PhenoLab - an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants [J].
Amby, D. B. ;
Westergaard, J. C. ;
Grosskinsky, D. K. ;
Jensen, S. M. ;
Svensgaard, J. ;
Liu, F. ;
Christensen, S. ;
Roitsch, T. .
SMART AGRICULTURAL TECHNOLOGY, 2025, 11
[24]   UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions [J].
Chivasa, Walter ;
Mutanga, Onisimo ;
Biradar, Chandrashekhar .
REMOTE SENSING, 2020, 12 (15)
[25]   Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles [J].
Hu, Yuncai ;
Knapp, Samuel ;
Schmidhalter, Urs .
REMOTE SENSING, 2020, 12 (03)
[26]   Resilience of UK crop yields to compound climate change [J].
Slater, Louise J. ;
Huntingford, Chris ;
Pywell, Richard F. ;
Redhead, John W. ;
Kendon, Elizabeth J. .
EARTH SYSTEM DYNAMICS, 2022, 13 (03) :1377-1396
[27]   Editorial: High-Throughput Phenotyping in the Genomic Improvement of Livestock [J].
Silva, Fabyano Fonseca ;
Morota, Gota ;
Rosa, Guilherme Jordao de Magalhaes .
FRONTIERS IN GENETICS, 2021, 12
[28]   Global Role of Crop Genomics in the Face of Climate Change [J].
Pourkheirandish, Mohammad ;
Golicz, Agnieszka A. ;
Bhalla, Prem L. ;
Singh, Mohan B. .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[29]   IJCropSeed: An open-access tool for high-throughput analysis of crop seed radiographs [J].
de Medeiros, Andre Dantas ;
da Silva, Laercio Junio ;
da Silva, Jose Maria ;
Fernandes dos Santos Dias, Denise Cunha ;
Pereira, Marcio Dias .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 175 (175)
[30]   Advancing crop resilience through nucleic acid innovations: rhizosphere engineering for food security and climate adaptation [J].
Saeed, Qudsia ;
Mustafa, Adnan ;
Ali, Shahzaib ;
Tobiloba, Lasisi Hammed ;
Rebi, Ansa ;
Baloch, Sadia Babar ;
Mumtaz, Muhammad Zahid ;
Naveed, Muhammad ;
Farooq, Muhammad ;
Lu, Xiankai .
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES, 2025, 310