139 related articles for article (PubMed ID: 30405675)
21. Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning.
Sharma P; Leigh L; Chang J; Maimaitijiang M; Caffé M
Sensors (Basel); 2022 Jan; 22(2):. PubMed ID: 35062559
[TBL] [Abstract][Full Text] [Related]
22. Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies.
Wang X; Singh D; Marla S; Morris G; Poland J
Plant Methods; 2018; 14():53. PubMed ID: 29997682
[TBL] [Abstract][Full Text] [Related]
23. Phenotyping Flowering in Canola (
Zhang T; Vail S; Duddu HSN; Parkin IAP; Guo X; Johnson EN; Shirtliffe SJ
Front Plant Sci; 2021; 12():686332. PubMed ID: 34220907
[TBL] [Abstract][Full Text] [Related]
24. A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.
Wan L; Zhu J; Du X; Zhang J; Han X; Zhou W; Li X; Liu J; Liang F; He Y; Cen H
J Exp Bot; 2021 Jun; 72(13):4691-4707. PubMed ID: 33963382
[TBL] [Abstract][Full Text] [Related]
25. Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images.
Fernandez-Gallego JA; Kefauver SC; Gutiérrez NA; Nieto-Taladriz MT; Araus JL
Plant Methods; 2018; 14():22. PubMed ID: 29568319
[TBL] [Abstract][Full Text] [Related]
26. Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.
Lee HS; Shin BS; Thomasson JA; Wang T; Zhang Z; Han X
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214326
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.
Borra-Serrano I; Peña JM; Torres-Sánchez J; Mesas-Carrascosa FJ; López-Granados F
Sensors (Basel); 2015 Aug; 15(8):19688-708. PubMed ID: 26274960
[TBL] [Abstract][Full Text] [Related]
29. Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning.
Ding R; Luo J; Wang C; Yu L; Yang J; Wang M; Zhong S; Gu R
Plant Methods; 2023 Apr; 19(1):38. PubMed ID: 37005675
[TBL] [Abstract][Full Text] [Related]
30. High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques.
de Castro AI; Rallo P; Suárez MP; Torres-Sánchez J; Casanova L; Jiménez-Brenes FM; Morales-Sillero A; Jiménez MR; López-Granados F
Front Plant Sci; 2019; 10():1472. PubMed ID: 31803210
[TBL] [Abstract][Full Text] [Related]
31. Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.
Trujillano F; Jimenez G; Manrique E; Kahamba NF; Okumu F; Apollinaire N; Carrasco-Escobar G; Barrett B; Fornace K
Int J Health Geogr; 2024 May; 23(1):13. PubMed ID: 38764024
[TBL] [Abstract][Full Text] [Related]
32. Estimation of crop plant density at early mixed growth stages using UAV imagery.
Koh JCO; Hayden M; Daetwyler H; Kant S
Plant Methods; 2019; 15():64. PubMed ID: 31249606
[TBL] [Abstract][Full Text] [Related]
33. Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images.
Bai X; Liu P; Cao Z; Lu H; Xiong H; Yang A; Cai Z; Wang J; Yao J
Plant Phenomics; 2023; 5():0020. PubMed ID: 37040495
[TBL] [Abstract][Full Text] [Related]
34. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.
Sarkar S; Zhou J; Scaboo A; Zhou J; Aloysius N; Lim TT
Plants (Basel); 2023 Aug; 12(16):. PubMed ID: 37631105
[TBL] [Abstract][Full Text] [Related]
35. High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery.
Ostos-Garrido FJ; de Castro AI; Torres-Sánchez J; Pistón F; Peña JM
Front Plant Sci; 2019; 10():948. PubMed ID: 31396251
[TBL] [Abstract][Full Text] [Related]
36. An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images.
Khan Z; Miklavcic SJ
Front Plant Sci; 2019; 10():683. PubMed ID: 31191586
[TBL] [Abstract][Full Text] [Related]
37. Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits.
Shu M; Fei S; Zhang B; Yang X; Guo Y; Li B; Ma Y
Plant Phenomics; 2022; 2022():9802585. PubMed ID: 36158531
[TBL] [Abstract][Full Text] [Related]
38. Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV.
de Oliveira DC; Wehrmeister MA
Sensors (Basel); 2018 Jul; 18(7):. PubMed ID: 30002290
[TBL] [Abstract][Full Text] [Related]
39.
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]
40. Applications of Unmanned Aerial Vehicle Based Imagery in Turfgrass Field Trials.
Zhang J; Virk S; Porter W; Kenworthy K; Sullivan D; Schwartz B
Front Plant Sci; 2019; 10():279. PubMed ID: 30930917
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
