175 related articles for article (PubMed ID: 37790779)
21. Field rice panicle detection and counting based on deep learning.
Wang X; Yang W; Lv Q; Huang C; Liang X; Chen G; Xiong L; Duan L
Front Plant Sci; 2022; 13():966495. PubMed ID: 36035660
[TBL] [Abstract][Full Text] [Related]
22. Identification of
Li R; Li Z; Ye J; Yang Y; Ye J; Xu S; Liu J; Yuan X; Wang Y; Zhang M; Yu H; Xu Q; Wang S; Yang Y; Wang S; Wei X; Feng Y
Front Plant Sci; 2022; 13():880919. PubMed ID: 35548297
[TBL] [Abstract][Full Text] [Related]
23. Detection of QTLs for panicle-related traits using an
Li G; Cheng Y; Yin M; Yang J; Ying J; Zhu C
PeerJ; 2021; 9():e12504. PubMed ID: 34909275
[TBL] [Abstract][Full Text] [Related]
24. Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field.
Guo Z; Yang C; Yang W; Chen G; Jiang Z; Wang B; Zhang J
J Exp Bot; 2022 Nov; 73(19):6575-6588. PubMed ID: 35776094
[TBL] [Abstract][Full Text] [Related]
25. Size measurement and filled/unfilled detection of rice grains using backlight image processing.
Feng X; Wang Z; Zeng Z; Zhou Y; Lan Y; Zou W; Gong H; Qi L
Front Plant Sci; 2023; 14():1213486. PubMed ID: 37900751
[TBL] [Abstract][Full Text] [Related]
26. Development of New Restorer Lines Carrying Some Restoring Fertility Genes with Flowering, Yield and Grains Quality Characteristics in Rice (
Awad-Allah MMA; Elekhtyar NM; El-Abd MA; Abdelkader MFM; Mahmoud MH; Mohamed AH; El-Diasty MZ; Said MM; Shamseldin SAM; Abdein MA
Genes (Basel); 2022 Mar; 13(3):. PubMed ID: 35328012
[TBL] [Abstract][Full Text] [Related]
27. Novel Imaging Techniques to Analyze Panicle Architecture.
Pasion E; Aguila R; Sreenivasulu N; Anacleto R
Methods Mol Biol; 2019; 1892():75-88. PubMed ID: 30397800
[TBL] [Abstract][Full Text] [Related]
28. [Effects of ozone stress on amylose content and starch RVA profile in grains located at diffe-rent positions on a panicle].
Zhang YL; Mu HR; Shao ZS; Wang YX; Jing LQ; Wang YL; Yang LX
Ying Yong Sheng Tai Xue Bao; 2019 Dec; 30(12):4211-4221. PubMed ID: 31840467
[TBL] [Abstract][Full Text] [Related]
29. Effect of qGN4.1 QTL for Grain Number per Panicle in Genetic Backgrounds of Twelve Different Mega Varieties of Rice.
Singh VK; Ellur RK; Singh AK; Nagarajan M; Singh BD; Singh NK
Rice (N Y); 2018 Jan; 11(1):8. PubMed ID: 29356995
[TBL] [Abstract][Full Text] [Related]
30. QTL analysis of novel genomic regions associated with yield and yield related traits in new plant type based recombinant inbred lines of rice (Oryza sativa L.).
Marathi B; Guleria S; Mohapatra T; Parsad R; Mariappan N; Kurungara VK; Atwal SS; Prabhu KV; Singh NK; Singh AK
BMC Plant Biol; 2012 Aug; 12():137. PubMed ID: 22876968
[TBL] [Abstract][Full Text] [Related]
31. First Report of Bacterial Panicle Blight of Rice Caused by Burkholderia glumae in South Africa.
Zhou XG
Plant Dis; 2014 Apr; 98(4):566. PubMed ID: 30708715
[TBL] [Abstract][Full Text] [Related]
32. Dissecting the genetic basis of heavy panicle hybrid rice uncovered Gn1a and GS3 as key genes.
Wang S; Ma B; Gao Q; Jiang G; Zhou L; Tu B; Qin P; Tan X; Liu P; Kang Y; Wang Y; Chen W; Liang C; Li S
Theor Appl Genet; 2018 Jun; 131(6):1391-1403. PubMed ID: 29546444
[TBL] [Abstract][Full Text] [Related]
33. Uncovering the genetic mechanisms regulating panicle architecture in rice with GPWAS and GWAS.
Zhong H; Liu S; Meng X; Sun T; Deng Y; Kong W; Peng Z; Li Y
BMC Genomics; 2021 Jan; 22(1):86. PubMed ID: 33509071
[TBL] [Abstract][Full Text] [Related]
34. Effects of Grain Shape Genes Editing on Appearance Quality of Erect-Panicle Geng/Japonica Rice.
Mao T; Zhu M; Sheng Z; Shao G; Jiao G; Mawia AM; Ahmad S; Xie L; Tang S; Wei X; Hu S; Hu P
Rice (N Y); 2021 Aug; 14(1):74. PubMed ID: 34374880
[TBL] [Abstract][Full Text] [Related]
35. Genome wide association studies on seven yield-related traits of 183 rice varieties in Bangladesh.
Roy N; Kabir AH; Zahan N; Mouna ST; Chakravarty S; Rahman AH; Bayzid MS
Plant Direct; 2024 Jun; 8(6):e593. PubMed ID: 38887667
[TBL] [Abstract][Full Text] [Related]
36. High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama.
Crowell S; Falcão AX; Shah A; Wilson Z; Greenberg AJ; McCouch SR
Plant Physiol; 2014 Jun; 165(2):479-495. PubMed ID: 24696519
[TBL] [Abstract][Full Text] [Related]
37. Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization.
Xiong X; Duan L; Liu L; Tu H; Yang P; Wu D; Chen G; Xiong L; Yang W; Liu Q
Plant Methods; 2017; 13():104. PubMed ID: 29209408
[TBL] [Abstract][Full Text] [Related]
38. Automatic estimation of heading date of paddy rice using deep learning.
Desai SV; Balasubramanian VN; Fukatsu T; Ninomiya S; Guo W
Plant Methods; 2019; 15():76. PubMed ID: 31338116
[TBL] [Abstract][Full Text] [Related]
39. Controlling the trade-off between spikelet number and grain filling: the hierarchy of starch synthesis in spikelets of rice panicle in relation to hormone dynamics.
Panigrahi R; Kariali E; Panda BB; Lafarge T; Mohapatra PK
Funct Plant Biol; 2019 Jun; 46(6):507-523. PubMed ID: 30961785
[TBL] [Abstract][Full Text] [Related]
40. The LARGE2-APO1/APO2 regulatory module controls panicle size and grain number in rice.
Huang L; Hua K; Xu R; Zeng D; Wang R; Dong G; Zhang G; Lu X; Fang N; Wang D; Duan P; Zhang B; Liu Z; Li N; Luo Y; Qian Q; Yao S; Li Y
Plant Cell; 2021 May; 33(4):1212-1228. PubMed ID: 33693937
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
