115 related articles for article (PubMed ID: 38903425)
1. Estimation of soybean yield based on high-throughput phenotyping and machine learning.
Li X; Chen M; He S; Xu X; He L; Wang L; Gao Y; Tang F; Gong T; Wang W; Xu M; Liu C; Yu L; Liu W; Yang W
Front Plant Sci; 2024; 15():1395760. PubMed ID: 38903425
[TBL] [Abstract][Full Text] [Related]
2. A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications.
Mathew J; Delavarpour N; Miranda C; Stenger J; Zhang Z; Aduteye J; Flores P
Sensors (Basel); 2023 Jul; 23(14):. PubMed ID: 37514799
[TBL] [Abstract][Full Text] [Related]
3. Soybean leaf estimation based on RGB images and machine learning methods.
Li X; Xu X; Xiang S; Chen M; He S; Wang W; Xu M; Liu C; Yu L; Liu W; Yang W
Plant Methods; 2023 Jun; 19(1):59. PubMed ID: 37330499
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.
Duc NT; Ramlal A; Rajendran A; Raju D; Lal SK; Kumar S; Sahoo RN; Chinnusamy V
Front Plant Sci; 2023; 14():1206357. PubMed ID: 37771485
[TBL] [Abstract][Full Text] [Related]
6. Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean.
Herrero-Huerta M; Rodriguez-Gonzalvez P; Rainey KM
Plant Methods; 2020; 16():78. PubMed ID: 32514286
[TBL] [Abstract][Full Text] [Related]
7. High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.
Ranđelović P; Đorđević V; Miladinović J; Prodanović S; Ćeran M; Vollmann J
Plant Methods; 2023 Aug; 19(1):89. PubMed ID: 37633921
[TBL] [Abstract][Full Text] [Related]
8. Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles.
Bai D; Li D; Zhao C; Wang Z; Shao M; Guo B; Liu Y; Wang Q; Li J; Guo S; Wang R; Li YH; Qiu LJ; Jin X
Front Plant Sci; 2022; 13():1012293. PubMed ID: 36589058
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.
Sun X; Yang Z; Su P; Wei K; Wang Z; Yang C; Wang C; Qin M; Xiao L; Yang W; Zhang M; Song X; Feng M
Front Plant Sci; 2023; 14():1158837. PubMed ID: 37063231
[TBL] [Abstract][Full Text] [Related]
11. Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.
Yoosefzadeh-Najafabadi M; Earl HJ; Tulpan D; Sulik J; Eskandari M
Front Plant Sci; 2020; 11():624273. PubMed ID: 33510761
[TBL] [Abstract][Full Text] [Related]
12. Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters.
Sun T; Li Z; Wang Z; Liu Y; Zhu Z; Zhao Y; Xie W; Cui S; Chen G; Yang W; Zhang Z; Zhang F
Plants (Basel); 2024 Jan; 13(1):. PubMed ID: 38202447
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery.
Yuan W; Wijewardane NK; Jenkins S; Bai G; Ge Y; Graef GL
Sci Rep; 2019 Oct; 9(1):14089. PubMed ID: 31575995
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits.
Yoosefzadeh-Najafabadi M; Tulpan D; Eskandari M
PLoS One; 2021; 16(4):e0250665. PubMed ID: 33930039
[TBL] [Abstract][Full Text] [Related]
17. Soybean (
Yang W; Li Z; Chen G; Cui S; Wu Y; Liu X; Meng W; Liu Y; He J; Liu D; Zhou Y; Tang Z; Xiang Y; Zhang F
Plants (Basel); 2024 May; 13(11):. PubMed ID: 38891307
[TBL] [Abstract][Full Text] [Related]
18. Improving the efficiency of soybean breeding with high-throughput canopy phenotyping.
Moreira FF; Hearst AA; Cherkauer KA; Rainey KM
Plant Methods; 2019; 15():139. PubMed ID: 31827576
[TBL] [Abstract][Full Text] [Related]
19. Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image.
Ma Y; Ma L; Zhang Q; Huang C; Yi X; Chen X; Hou T; Lv X; Zhang Z
Front Plant Sci; 2022; 13():925986. PubMed ID: 35783985
[TBL] [Abstract][Full Text] [Related]
20. Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.
Zhou J; Beche E; Vieira CC; Yungbluth D; Zhou J; Scaboo A; Chen P
Front Plant Sci; 2021; 12():768742. PubMed ID: 35087547
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]