197 related articles for article (PubMed ID: 29403522)
1. In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR.
Sun S; Li C; Paterson AH; Jiang Y; Xu R; Robertson JS; Snider JL; Chee PW
Front Plant Sci; 2018; 9():16. PubMed ID: 29403522
[TBL] [Abstract][Full Text] [Related]
2. Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera.
Jiang Y; Li C; Paterson AH; Sun S; Xu R; Robertson J
Front Plant Sci; 2017; 8():2233. PubMed ID: 29441074
[TBL] [Abstract][Full Text] [Related]
3. Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding.
Walter JDC; Edwards J; McDonald G; Kuchel H
Front Plant Sci; 2019; 10():1145. PubMed ID: 31611889
[TBL] [Abstract][Full Text] [Related]
4. The effects of sampling and instrument orientation on LiDAR data from crop plots.
Khorsandi A; Tanino K; Noble SD
Front Plant Sci; 2023; 14():1087239. PubMed ID: 36998694
[TBL] [Abstract][Full Text] [Related]
5. Development of a Peanut Canopy Measurement System Using a Ground-Based LiDAR Sensor.
Yuan H; Bennett RS; Wang N; Chamberlin KD
Front Plant Sci; 2019; 10():203. PubMed ID: 30873193
[TBL] [Abstract][Full Text] [Related]
6. High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR.
Jimenez-Berni JA; Deery DM; Rozas-Larraondo P; Condon ATG; Rebetzke GJ; James RA; Bovill WD; Furbank RT; Sirault XRR
Front Plant Sci; 2018; 9():237. PubMed ID: 29535749
[TBL] [Abstract][Full Text] [Related]
7. On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System.
Moreno H; Valero C; Bengochea-Guevara JM; Ribeiro Á; Garrido-Izard M; Andújar D
Sensors (Basel); 2020 Feb; 20(4):. PubMed ID: 32085436
[TBL] [Abstract][Full Text] [Related]
8. High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates.
Madec S; Baret F; de Solan B; Thomas S; Dutartre D; Jezequel S; Hemmerlé M; Colombeau G; Comar A
Front Plant Sci; 2017; 8():2002. PubMed ID: 29230229
[TBL] [Abstract][Full Text] [Related]
9. Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data.
Jing L; Wei X; Song Q; Wang F
Sensors (Basel); 2023 Oct; 23(19):. PubMed ID: 37837163
[TBL] [Abstract][Full Text] [Related]
10. Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail.
Cai S; Gou W; Wen W; Lu X; Fan J; Guo X
Plants (Basel); 2023 Jan; 12(3):. PubMed ID: 36771568
[TBL] [Abstract][Full Text] [Related]
11. Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations.
Hui F; Zhu J; Hu P; Meng L; Zhu B; Guo Y; Li B; Ma Y
Ann Bot; 2018 Apr; 121(5):1079-1088. PubMed ID: 29509841
[TBL] [Abstract][Full Text] [Related]
12. Correction of UAV LiDAR-derived grassland canopy height based on scan angle.
Xu C; Zhao D; Zheng Z; Zhao P; Chen J; Li X; Zhao X; Zhao Y; Liu W; Wu B; Zeng Y
Front Plant Sci; 2023; 14():1108109. PubMed ID: 37021312
[TBL] [Abstract][Full Text] [Related]
13. Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a "Phenomobile".
Qiu Q; Sun N; Bai H; Wang N; Fan Z; Wang Y; Meng Z; Li B; Cong Y
Front Plant Sci; 2019; 10():554. PubMed ID: 31134110
[TBL] [Abstract][Full Text] [Related]
14. High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV.
Li F; Piasecki C; Millwood RJ; Wolfe B; Mazarei M; Stewart CN
Front Plant Sci; 2020; 11():574073. PubMed ID: 33193511
[TBL] [Abstract][Full Text] [Related]
15. Deployment of Lidar from a Ground Platform: Customizing a Low-Cost, Information-Rich and User-Friendly Application for Field Phenomics Research.
Heun JT; Attalah S; French AN; Lehner KR; McKay JK; Mullen JL; Ottman MJ; Andrade-Sanchez P
Sensors (Basel); 2019 Dec; 19(24):. PubMed ID: 31817334
[TBL] [Abstract][Full Text] [Related]
16. Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach.
Han L; Yang G; Yang H; Xu B; Li Z; Yang X
Front Plant Sci; 2018; 9():1638. PubMed ID: 30483291
[TBL] [Abstract][Full Text] [Related]
17. A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum.
Thapa S; Zhu F; Walia H; Yu H; Ge Y
Sensors (Basel); 2018 Apr; 18(4):. PubMed ID: 29652788
[TBL] [Abstract][Full Text] [Related]
18. Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.
Song P; Li Z; Yang M; Shao Y; Pu Z; Yang W; Zhai R
Front Plant Sci; 2023; 14():1097725. PubMed ID: 36778701
[TBL] [Abstract][Full Text] [Related]
19. Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.
Nguyen PT; Shi F; Wang J; Badenhorst PE; Spangenberg GC; Smith KF; Daetwyler HD
Front Plant Sci; 2022; 13():950720. PubMed ID: 36003811
[TBL] [Abstract][Full Text] [Related]
20. Erratum: High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay.
J Vis Exp; 2023 Oct; (200):. PubMed ID: 37851522
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
