120 related articles for article (PubMed ID: 31615044)
1. 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]
2. Estimating plant distance in maize using Unmanned Aerial Vehicle (UAV).
Zhang J; Basso B; Price RF; Putman G; Shuai G
PLoS One; 2018; 13(4):e0195223. PubMed ID: 29677204
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery.
Falco N; Wainwright HM; Dafflon B; Ulrich C; Soom F; Peterson JE; Brown JB; Schaettle KB; Williamson M; Cothren JD; Ham RG; McEntire JA; Hubbard SS
Sci Rep; 2021 Mar; 11(1):7046. PubMed ID: 33782488
[TBL] [Abstract][Full Text] [Related]
6. Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.
Bellis ES; Hashem AA; Causey JL; Runkle BRK; Moreno-García B; Burns BW; Green VS; Burcham TN; Reba ML; Huang X
Front Plant Sci; 2022; 13():716506. PubMed ID: 35401643
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images.
Tunca E; Köksal ES; Çetin S; Ekiz NM; Balde H
Environ Monit Assess; 2018 Oct; 190(11):682. PubMed ID: 30374821
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review.
Librán-Embid F; Klaus F; Tscharntke T; Grass I
Sci Total Environ; 2020 Aug; 732():139204. PubMed ID: 32438190
[TBL] [Abstract][Full Text] [Related]
11. Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis.
Duan B; Fang S; Zhu R; Wu X; Wang S; Gong Y; Peng Y
Front Plant Sci; 2019; 10():204. PubMed ID: 30873194
[TBL] [Abstract][Full Text] [Related]
12. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images.
Bouguettaya A; Zarzour H; Kechida A; Taberkit AM
Cluster Comput; 2023; 26(2):1297-1317. PubMed ID: 35968221
[TBL] [Abstract][Full Text] [Related]
13. Deep learning techniques to classify agricultural crops through UAV imagery: a review.
Bouguettaya A; Zarzour H; Kechida A; Taberkit AM
Neural Comput Appl; 2022; 34(12):9511-9536. PubMed ID: 35281624
[TBL] [Abstract][Full Text] [Related]
14. UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status.
Yao L; Wang Q; Yang J; Zhang Y; Zhu Y; Cao W; Ni J
Sensors (Basel); 2019 Feb; 19(4):. PubMed ID: 30781552
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature.
Perich G; Hund A; Anderegg J; Roth L; Boer MP; Walter A; Liebisch F; Aasen H
Front Plant Sci; 2020; 11():150. PubMed ID: 32158459
[TBL] [Abstract][Full Text] [Related]
17. Unmanned aerial vehicles for surveying marine fauna: assessing detection probability.
Hodgson A; Peel D; Kelly N
Ecol Appl; 2017 Jun; 27(4):1253-1267. PubMed ID: 28178755
[TBL] [Abstract][Full Text] [Related]
18. In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery.
Niu H; Peddagudreddygari JR; Bhandari M; Landivar JA; Bednarz CW; Duffield N
Sensors (Basel); 2024 Apr; 24(8):. PubMed ID: 38676047
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging.
Makanza R; Zaman-Allah M; Cairns JE; Magorokosho C; Tarekegne A; Olsen M; Prasanna BM
Remote Sens (Basel); 2018 Feb; 10(2):330. PubMed ID: 33489316
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]