384 related articles for article (PubMed ID: 30298081)
1. Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery.
Zhao B; Zhang J; Yang C; Zhou G; Ding Y; Shi Y; Zhang D; Xie J; Liao Q
Front Plant Sci; 2018; 9():1362. PubMed ID: 30298081
[TBL] [Abstract][Full Text] [Related]
2. Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks.
Zhang J; Zhao B; Yang C; Shi Y; Liao Q; Zhou G; Wang C; Xie T; Jiang Z; Zhang D; Yang W; Huang C; Xie J
Front Plant Sci; 2020; 11():617. PubMed ID: 32587594
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. The estimation of crop emergence in potatoes by UAV RGB imagery.
Li B; Xu X; Han J; Zhang L; Bian C; Jin L; Liu J
Plant Methods; 2019; 15():15. PubMed ID: 30792752
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field.
Wang L; Xiang L; Tang L; Jiang H
Sensors (Basel); 2021 Jan; 21(2):. PubMed ID: 33450839
[TBL] [Abstract][Full Text] [Related]
7. Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery.
Li J; Li Y; Qiao J; Li L; Wang X; Yao J; Liao G
Front Plant Sci; 2023; 14():1101143. PubMed ID: 36798713
[TBL] [Abstract][Full Text] [Related]
8. Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.
Dhakal R; Maimaitijiang M; Chang J; Caffe M
Sensors (Basel); 2023 Dec; 23(24):. PubMed ID: 38139554
[TBL] [Abstract][Full Text] [Related]
9. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (
Selvaraj MG; Valderrama M; Guzman D; Valencia M; Ruiz H; Acharjee A
Plant Methods; 2020; 16():87. PubMed ID: 32549903
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops.
Hu P; Chapman SC; Zheng B
Funct Plant Biol; 2021 Jul; 48(8):766-779. PubMed ID: 33663681
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. High-throughput unmanned aerial vehicle-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field.
Li J; Xie T; Chen Y; Zhang Y; Wang C; Jiang Z; Yang W; Zhou G; Guo L; Zhang J
J Exp Bot; 2022 Sep; 73(15):5264-5278. PubMed ID: 35641129
[TBL] [Abstract][Full Text] [Related]
15. Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV).
Shuai G; Martinez-Feria RA; Zhang J; Li S; Price R; Basso B
Sensors (Basel); 2019 Oct; 19(20):. PubMed ID: 31615044
[TBL] [Abstract][Full Text] [Related]
16. An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery.
Zhang B; Zhao D
Sensors (Basel); 2023 Jul; 23(15):. PubMed ID: 37571446
[TBL] [Abstract][Full Text] [Related]
17. Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region.
Sapkota S; Paudyal DR
Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420599
[TBL] [Abstract][Full Text] [Related]
18. Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (
Quirós Vargas JJ; Zhang C; Smitchger JA; McGee RJ; Sankaran S
Sensors (Basel); 2019 Apr; 19(9):. PubMed ID: 31052251
[TBL] [Abstract][Full Text] [Related]
19. Ramie Yield Estimation Based on UAV RGB Images.
Fu H; Wang C; Cui G; She W; Zhao L
Sensors (Basel); 2021 Jan; 21(2):. PubMed ID: 33477949
[TBL] [Abstract][Full Text] [Related]
20. High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.
Volpato L; Pinto F; González-Pérez L; Thompson IG; Borém A; Reynolds M; Gérard B; Molero G; Rodrigues FA
Front Plant Sci; 2021; 12():591587. PubMed ID: 33664755
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
