171 related articles for article (PubMed ID: 35498682)
1. Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution
Buxbaum N; Lieth JH; Earles M
Front Plant Sci; 2022; 13():758818. PubMed ID: 35498682
[TBL] [Abstract][Full Text] [Related]
2. SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.
Misra T; Arora A; Marwaha S; Chinnusamy V; Rao AR; Jain R; Sahoo RN; Ray M; Kumar S; Raju D; Jha RR; Nigam A; Goel S
Plant Methods; 2020; 16():40. PubMed ID: 32206080
[TBL] [Abstract][Full Text] [Related]
3. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.
Yu S; Fan J; Lu X; Wen W; Shao S; Guo X; Zhao C
Front Plant Sci; 2022; 13():927832. PubMed ID: 35845657
[TBL] [Abstract][Full Text] [Related]
4. Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley.
Rueda-Ayala VP; Peña JM; Höglind M; Bengochea-Guevara JM; Andújar D
Sensors (Basel); 2019 Jan; 19(3):. PubMed ID: 30696014
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.
Castro W; Marcato Junior J; Polidoro C; Osco LP; Gonçalves W; Rodrigues L; Santos M; Jank L; Barrios S; Valle C; Simeão R; Carromeu C; Silveira E; Jorge LAC; Matsubara E
Sensors (Basel); 2020 Aug; 20(17):. PubMed ID: 32858803
[TBL] [Abstract][Full Text] [Related]
7. Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.
Andújar D; Fernández-Quintanilla C; Dorado J
Sensors (Basel); 2015 Jun; 15(6):12999-3011. PubMed ID: 26053748
[TBL] [Abstract][Full Text] [Related]
8. Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis.
Schunck D; Magistri F; Rosu RA; Cornelißen A; Chebrolu N; Paulus S; Léon J; Behnke S; Stachniss C; Kuhlmann H; Klingbeil L
PLoS One; 2021; 16(8):e0256340. PubMed ID: 34407122
[TBL] [Abstract][Full Text] [Related]
9. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.
Singh B; Kumar S; Elangovan A; Vasht D; Arya S; Duc NT; Swami P; Pawar GS; Raju D; Krishna H; Sathee L; Dalal M; Sahoo RN; Chinnusamy V
Front Plant Sci; 2023; 14():1214801. PubMed ID: 37448870
[TBL] [Abstract][Full Text] [Related]
10. A spatio temporal spectral framework for plant stress phenotyping.
Khanna R; Schmid L; Walter A; Nieto J; Siegwart R; Liebisch F
Plant Methods; 2019; 15():13. PubMed ID: 30774703
[TBL] [Abstract][Full Text] [Related]
11. Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning.
Lin Z; Fu R; Ren G; Zhong R; Ying Y; Lin T
Front Plant Sci; 2022; 13():980581. PubMed ID: 36092436
[TBL] [Abstract][Full Text] [Related]
12. Enhancement of Plant Productivity in the Post-Genomics Era.
Thao NP; Tran LS
Curr Genomics; 2016 Aug; 17(4):295-6. PubMed ID: 27499678
[TBL] [Abstract][Full Text] [Related]
13. Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery.
Adke S; Li C; Rasheed KM; Maier FW
Sensors (Basel); 2022 May; 22(10):. PubMed ID: 35632096
[TBL] [Abstract][Full Text] [Related]
14. Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for
Xiong X; Zhang J; Guo D; Chang L; Huang D
Sensors (Basel); 2019 May; 19(11):. PubMed ID: 31146350
[TBL] [Abstract][Full Text] [Related]
15. A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth.
Bernotas G; Scorza LCT; Hansen MF; Hales IJ; Halliday KJ; Smith LN; Smith ML; McCormick AJ
Gigascience; 2019 May; 8(5):. PubMed ID: 31127811
[TBL] [Abstract][Full Text] [Related]
16. UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.
Nielsen KME; Duddu HSN; Bett KE; Shirtliffe SJ
Plants (Basel); 2022 Oct; 11(20):. PubMed ID: 36297713
[TBL] [Abstract][Full Text] [Related]
17. Low-Cost Three-Dimensional Modeling of Crop Plants.
Martinez-Guanter J; Ribeiro Á; Peteinatos GG; Pérez-Ruiz M; Gerhards R; Bengochea-Guevara JM; Machleb J; Andújar D
Sensors (Basel); 2019 Jun; 19(13):. PubMed ID: 31261757
[TBL] [Abstract][Full Text] [Related]
18. Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors.
Simko I; Hayes RJ; Furbank RT
Front Plant Sci; 2016; 7():1985. PubMed ID: 28083011
[TBL] [Abstract][Full Text] [Related]
19. Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.
Feng X; Zhan Y; Wang Q; Yang X; Yu C; Wang H; Tang Z; Jiang D; Peng C; He Y
Plant J; 2020 Mar; 101(6):1448-1461. PubMed ID: 31680357
[TBL] [Abstract][Full Text] [Related]
20. Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.
Ye Z; Tan X; Dai M; Lin Y; Chen X; Nie P; Ruan Y; Kong D
Front Plant Sci; 2023; 14():1165552. PubMed ID: 37332711
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
