187 related articles for article (PubMed ID: 31742599)
1. High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.
Wang X; Xuan H; Evers B; Shrestha S; Pless R; Poland J
Gigascience; 2019 Nov; 8(11):. PubMed ID: 31742599
[TBL] [Abstract][Full Text] [Related]
2. Detection and analysis of wheat spikes using Convolutional Neural Networks.
Hasan MM; Chopin JP; Laga H; Miklavcic SJ
Plant Methods; 2018; 14():100. PubMed ID: 30459822
[TBL] [Abstract][Full Text] [Related]
3. High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat.
Singh D; Wang X; Kumar U; Gao L; Noor M; Imtiaz M; Singh RP; Poland J
Front Plant Sci; 2019; 10():394. PubMed ID: 31019521
[TBL] [Abstract][Full Text] [Related]
4. SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.
Misra T; Arora A; Marwaha S; Chinnusamy V; Rao AR; Jain R; Sahoo RN; Ray M; Kumar S; Raju D; Jha RR; Nigam A; Goel S
Plant Methods; 2020; 16():40. PubMed ID: 32206080
[TBL] [Abstract][Full Text] [Related]
5. Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.
Zhan Y; Zhou Y; Bai G; Ge Y
Sensors (Basel); 2024 May; 24(11):. PubMed ID: 38894212
[TBL] [Abstract][Full Text] [Related]
6.
Sadeghi-Tehran P; Virlet N; Ampe EM; Reyns P; Hawkesford MJ
Front Plant Sci; 2019; 10():1176. PubMed ID: 31616456
[TBL] [Abstract][Full Text] [Related]
7. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
Pound MP; Atkinson JA; Townsend AJ; Wilson MH; Griffiths M; Jackson AS; Bulat A; Tzimiropoulos G; Wells DM; Murchie EH; Pridmore TP; French AP
Gigascience; 2017 Oct; 6(10):1-10. PubMed ID: 29020747
[TBL] [Abstract][Full Text] [Related]
8. Major flowering time genes of barley: allelic diversity, effects, and comparison with wheat.
Fernández-Calleja M; Casas AM; Igartua E
Theor Appl Genet; 2021 Jul; 134(7):1867-1897. PubMed ID: 33969431
[TBL] [Abstract][Full Text] [Related]
9. GWAS for plant growth stages and yield components in spring wheat (Triticum aestivum L.) harvested in three regions of Kazakhstan.
Turuspekov Y; Baibulatova A; Yermekbayev K; Tokhetova L; Chudinov V; Sereda G; Ganal M; Griffiths S; Abugalieva S
BMC Plant Biol; 2017 Nov; 17(Suppl 1):190. PubMed ID: 29143598
[TBL] [Abstract][Full Text] [Related]
10. Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat.
Gao J; Hu X; Gao C; Chen G; Feng H; Jia Z; Zhao P; Yu H; Li H; Geng Z; Fu J; Zhang J; Cheng Y; Yang B; Pang Z; Xiang D; Jia J; Su H; Mao H; Lan C; Chen W; Yan W; Gao L; Yang W; Li Q
Plant Biotechnol J; 2023 Oct; 21(10):1966-1977. PubMed ID: 37392004
[TBL] [Abstract][Full Text] [Related]
11. Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images.
Wang X; Silva P; Bello NM; Singh D; Evers B; Mondal S; Espinosa FP; Singh RP; Poland J
Front Plant Sci; 2020; 11():587093. PubMed ID: 33193537
[TBL] [Abstract][Full Text] [Related]
12. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.
Nguyen C; Sagan V; Bhadra S; Moose S
Sensors (Basel); 2023 Feb; 23(4):. PubMed ID: 36850425
[TBL] [Abstract][Full Text] [Related]
13. Deciphering the genetics of flowering time by an association study on candidate genes in bread wheat (Triticum aestivum L.).
Rousset M; Bonnin I; Remoué C; Falque M; Rhoné B; Veyrieras JB; Madur D; Murigneux A; Balfourier F; Le Gouis J; Santoni S; Goldringer I
Theor Appl Genet; 2011 Oct; 123(6):907-26. PubMed ID: 21761163
[TBL] [Abstract][Full Text] [Related]
14. RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.
Yasrab R; Atkinson JA; Wells DM; French AP; Pridmore TP; Pound MP
Gigascience; 2019 Nov; 8(11):. PubMed ID: 31702012
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.
Gill T; Gill SK; Saini DK; Chopra Y; de Koff JP; Sandhu KS
Phenomics; 2022 Jun; 2(3):156-183. PubMed ID: 36939773
[TBL] [Abstract][Full Text] [Related]
17. Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping.
Zhou S; Chai X; Yang Z; Wang H; Yang C; Sun T
Plant Methods; 2021 Apr; 17(1):48. PubMed ID: 33926480
[TBL] [Abstract][Full Text] [Related]
18. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat.
Zhu Y; Sun G; Ding G; Zhou J; Wen M; Jin S; Zhao Q; Colmer J; Ding Y; Ober ES; Zhou J
Plant Physiol; 2021 Oct; 187(2):716-738. PubMed ID: 34608970
[TBL] [Abstract][Full Text] [Related]
19. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning.
Yang Z; Gao S; Xiao F; Li G; Ding Y; Guo Q; Paul MJ; Liu Z
Plant Methods; 2020; 16():117. PubMed ID: 32863854
[TBL] [Abstract][Full Text] [Related]
20. Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.
Tian F; Vieira CC; Zhou J; Zhou J; Chen P
Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991952
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
