207 related articles for article (PubMed ID: 25793008)
1. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach.
Liebisch F; Kirchgessner N; Schneider D; Walter A; Hund A
Plant Methods; 2015; 11():9. PubMed ID: 25793008
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance under Phosphorus Fertilization.
Gracia-Romero A; Kefauver SC; Vergara-Díaz O; Zaman-Allah MA; Prasanna BM; Cairns JE; Araus JL
Front Plant Sci; 2017; 8():2004. PubMed ID: 29230230
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].
Wu Q; Sun H; Li MZ; Song YY; Zhang YE
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Jan; 35(1):178-83. PubMed ID: 25993844
[TBL] [Abstract][Full Text] [Related]
7. Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques.
Buchaillot ML; Gracia-Romero A; Vergara-Diaz O; Zaman-Allah MA; Tarekegne A; Cairns JE; Prasanna BM; Araus JL; Kefauver SC
Sensors (Basel); 2019 Apr; 19(8):. PubMed ID: 30995754
[TBL] [Abstract][Full Text] [Related]
8. Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines.
Potgieter AB; George-Jaeggli B; Chapman SC; Laws K; Suárez Cadavid LA; Wixted J; Watson J; Eldridge M; Jordan DR; Hammer GL
Front Plant Sci; 2017; 8():1532. PubMed ID: 28951735
[TBL] [Abstract][Full Text] [Related]
9. A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.
Vergara-Díaz O; Zaman-Allah MA; Masuka B; Hornero A; Zarco-Tejada P; Prasanna BM; Cairns JE; Araus JL
Front Plant Sci; 2016; 7():666. PubMed ID: 27242867
[TBL] [Abstract][Full Text] [Related]
10. Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.
Tolley SA; Carpenter N; Crawford MM; Delp EJ; Habib A; Tuinstra MR
Front Plant Sci; 2023; 14():1202536. PubMed ID: 37409309
[TBL] [Abstract][Full Text] [Related]
11. Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images.
Wang X; Silva P; Bello NM; Singh D; Evers B; Mondal S; Espinosa FP; Singh RP; Poland J
Front Plant Sci; 2020; 11():587093. PubMed ID: 33193537
[TBL] [Abstract][Full Text] [Related]
12. Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.
Elmetwalli AH; Tyler AN; Moghanm FS; Alamri SAM; Eid EM; Elsayed S
Sensors (Basel); 2021 Jun; 21(11):. PubMed ID: 34204099
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. LAI estimation through remotely sensed NDVI following hail defoliation in maize (
Furlanetto J; Dal Ferro N; Longo M; Sartori L; Polese R; Caceffo D; Nicoli L; Morari F
Precis Agric; 2023 Feb; ():1-25. PubMed ID: 37363793
[TBL] [Abstract][Full Text] [Related]
15. Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping.
Wang J; Li X; Guo T; Dzievit MJ; Yu X; Liu P; Price KP; Yu J
Plant Genome; 2021 Nov; 14(3):e20155. PubMed ID: 34596348
[TBL] [Abstract][Full Text] [Related]
16. Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe.
Gracia-Romero A; Vergara-Díaz O; Thierfelder C; Cairns JE; Kefauver SC; Araus JL
Remote Sens (Basel); 2018; 10(2):349. PubMed ID: 32704486
[TBL] [Abstract][Full Text] [Related]
17. Spectral reflectance from a soybean canopy exposed to elevated CO2 and O3.
Gray SB; Dermody O; DeLucia EH
J Exp Bot; 2010 Oct; 61(15):4413-22. PubMed ID: 20696654
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.
Zaman-Allah M; Vergara O; Araus JL; Tarekegne A; Magorokosho C; Zarco-Tejada PJ; Hornero A; Albà AH; Das B; Craufurd P; Olsen M; Prasanna BM; Cairns J
Plant Methods; 2015; 11():35. PubMed ID: 26106438
[TBL] [Abstract][Full Text] [Related]
20. A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.
Tattaris M; Reynolds MP; Chapman SC
Front Plant Sci; 2016; 7():1131. PubMed ID: 27536304
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
