209 related articles for article (PubMed ID: 34266447)
1. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery.
Tatsumi K; Igarashi N; Mengxue X
Plant Methods; 2021 Jul; 17(1):77. PubMed ID: 34266447
[TBL] [Abstract][Full Text] [Related]
2. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.
Johansen K; Morton MJL; Malbeteau Y; Aragon B; Al-Mashharawi S; Ziliani MG; Angel Y; Fiene G; Negrão S; Mousa MAA; Tester MA; McCabe MF
Front Artif Intell; 2020; 3():28. PubMed ID: 33733147
[TBL] [Abstract][Full Text] [Related]
3. Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery.
Li J; Veeranampalayam-Sivakumar AN; Bhatta M; Garst ND; Stoll H; Stephen Baenziger P; Belamkar V; Howard R; Ge Y; Shi Y
Plant Methods; 2019; 15():123. PubMed ID: 31695728
[TBL] [Abstract][Full Text] [Related]
4. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system.
Lu N; Zhou J; Han Z; Li D; Cao Q; Yao X; Tian Y; Zhu Y; Cao W; Cheng T
Plant Methods; 2019; 15():17. PubMed ID: 30828356
[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. 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]
7. Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.
Fei S; Hassan MA; Ma Y; Shu M; Cheng Q; Li Z; Chen Z; Xiao Y
Front Plant Sci; 2021; 12():730181. PubMed ID: 34987529
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.
Camenzind MP; Yu K
Front Plant Sci; 2023; 14():1214931. PubMed ID: 38235203
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.
Sun C; Feng L; Zhang Z; Ma Y; Crosby T; Naber M; Wang Y
Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32947919
[TBL] [Abstract][Full Text] [Related]
14. Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.
Pokhrel A; Virk S; Snider JL; Vellidis G; Hand LC; Sintim HY; Parkash V; Chalise DP; Lee JM; Byers C
Front Plant Sci; 2023; 14():1248152. PubMed ID: 37794937
[TBL] [Abstract][Full Text] [Related]
15. Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery.
Tang Z; Jin Y; Brown PH; Park M
Front Plant Sci; 2023; 14():1057733. PubMed ID: 37089640
[TBL] [Abstract][Full Text] [Related]
16. Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features.
Lin X; Chen J; Lou P; Yi S; Qin Y; You H; Han X
Plant Methods; 2021 Sep; 17(1):96. PubMed ID: 34535179
[TBL] [Abstract][Full Text] [Related]
17. Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm.
Pan W; Wang X; Sun Y; Wang J; Li Y; Li S
Plant Methods; 2023 Jan; 19(1):7. PubMed ID: 36691062
[TBL] [Abstract][Full Text] [Related]
18. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.
Fei S; Hassan MA; Xiao Y; Su X; Chen Z; Cheng Q; Duan F; Chen R; Ma Y
Precis Agric; 2023; 24(1):187-212. PubMed ID: 35967193
[TBL] [Abstract][Full Text] [Related]
19. Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV.
Guo Y; Wang H; Wu Z; Wang S; Sun H; Senthilnath J; Wang J; Robin Bryant C; Fu Y
Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32899582
[TBL] [Abstract][Full Text] [Related]
20. UAV-based individual Chinese cabbage weight prediction using multi-temporal data.
Aguilar-Ariza A; Ishii M; Miyazaki T; Saito A; Khaing HP; Phoo HW; Kondo T; Fujiwara T; Guo W; Kamiya T
Sci Rep; 2023 Nov; 13(1):20122. PubMed ID: 37978327
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]