410 related articles for article (PubMed ID: 28738313)
1. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field.
Shakoor N; Lee S; Mockler TC
Curr Opin Plant Biol; 2017 Aug; 38():184-192. PubMed ID: 28738313
[TBL] [Abstract][Full Text] [Related]
2. Field high-throughput phenotyping: the new crop breeding frontier.
Araus JL; Cairns JE
Trends Plant Sci; 2014 Jan; 19(1):52-61. PubMed ID: 24139902
[TBL] [Abstract][Full Text] [Related]
3. Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping.
Guo Q; Wu F; Pang S; Zhao X; Chen L; Liu J; Xue B; Xu G; Li L; Jing H; Chu C
Sci China Life Sci; 2018 Mar; 61(3):328-339. PubMed ID: 28616808
[TBL] [Abstract][Full Text] [Related]
4. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement.
Jangra S; Chaudhary V; Yadav RC; Yadav NR
Phenomics; 2021 Apr; 1(2):31-53. PubMed ID: 36939738
[TBL] [Abstract][Full Text] [Related]
5. Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops.
Furbank RT; Jimenez-Berni JA; George-Jaeggli B; Potgieter AB; Deery DM
New Phytol; 2019 Sep; 223(4):1714-1727. PubMed ID: 30937909
[TBL] [Abstract][Full Text] [Related]
6. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.
Mochida K; Koda S; Inoue K; Hirayama T; Tanaka S; Nishii R; Melgani F
Gigascience; 2019 Jan; 8(1):. PubMed ID: 30520975
[TBL] [Abstract][Full Text] [Related]
7. Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives.
Rasheed A; Hao Y; Xia X; Khan A; Xu Y; Varshney RK; He Z
Mol Plant; 2017 Aug; 10(8):1047-1064. PubMed ID: 28669791
[TBL] [Abstract][Full Text] [Related]
8. Future-proof crops: challenges and strategies for climate resilience improvement.
Kissoudis C; van de Wiel C; Visser RG; van der Linden G
Curr Opin Plant Biol; 2016 Apr; 30():47-56. PubMed ID: 26874966
[TBL] [Abstract][Full Text] [Related]
9. Physiological phenotyping of plants for crop improvement.
Ghanem ME; Marrou H; Sinclair TR
Trends Plant Sci; 2015 Mar; 20(3):139-44. PubMed ID: 25524213
[TBL] [Abstract][Full Text] [Related]
10. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives.
Yang W; Feng H; Zhang X; Zhang J; Doonan JH; Batchelor WD; Xiong L; Yan J
Mol Plant; 2020 Feb; 13(2):187-214. PubMed ID: 31981735
[TBL] [Abstract][Full Text] [Related]
11. Field phenotyping for African crops: overview and perspectives.
Cudjoe DK; Virlet N; Castle M; Riche AB; Mhada M; Waine TW; Mohareb F; Hawkesford MJ
Front Plant Sci; 2023; 14():1219673. PubMed ID: 37860243
[TBL] [Abstract][Full Text] [Related]
12. High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge.
Cabrera-Bosquet L; Crossa J; von Zitzewitz J; Serret MD; Araus JL
J Integr Plant Biol; 2012 May; 54(5):312-20. PubMed ID: 22420640
[TBL] [Abstract][Full Text] [Related]
13. Scaling up high-throughput phenotyping for abiotic stress selection in the field.
Smith DT; Potgieter AB; Chapman SC
Theor Appl Genet; 2021 Jun; 134(6):1845-1866. PubMed ID: 34076731
[TBL] [Abstract][Full Text] [Related]
14. Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches.
Nguyen GN; Kant S
Funct Plant Biol; 2018 May; 45(6):606-619. PubMed ID: 32290963
[TBL] [Abstract][Full Text] [Related]
15. Crop phenotyping in a context of global change: What to measure and how to do it.
Araus JL; Kefauver SC; Vergara-Díaz O; Gracia-Romero A; Rezzouk FZ; Segarra J; Buchaillot ML; Chang-Espino M; Vatter T; Sanchez-Bragado R; Fernandez-Gallego JA; Serret MD; Bort J
J Integr Plant Biol; 2022 Feb; 64(2):592-618. PubMed ID: 34807514
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning for High-Throughput Stress Phenotyping in Plants.
Singh A; Ganapathysubramanian B; Singh AK; Sarkar S
Trends Plant Sci; 2016 Feb; 21(2):110-124. PubMed ID: 26651918
[TBL] [Abstract][Full Text] [Related]
17. Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops.
Wasson AP; Richards RA; Chatrath R; Misra SC; Prasad SV; Rebetzke GJ; Kirkegaard JA; Christopher J; Watt M
J Exp Bot; 2012 May; 63(9):3485-98. PubMed ID: 22553286
[TBL] [Abstract][Full Text] [Related]
18. Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.
Feng X; Zhan Y; Wang Q; Yang X; Yu C; Wang H; Tang Z; Jiang D; Peng C; He Y
Plant J; 2020 Mar; 101(6):1448-1461. PubMed ID: 31680357
[TBL] [Abstract][Full Text] [Related]
19. Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production.
Dreccer MF; Molero G; Rivera-Amado C; John-Bejai C; Wilson Z
Plant Sci; 2019 May; 282():73-82. PubMed ID: 31003613
[TBL] [Abstract][Full Text] [Related]
20. Systematic Evaluation of Field Crop Performance Using Modern Phenotyping Tools and Techniques.
Boomsma CR; da Costa VA
Methods Mol Biol; 2019; 1864():419-440. PubMed ID: 30415350
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
