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1.
Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.
Sensors (Basel)
; 20(5)2020 Feb 27.
Artículo
en Inglés
| MEDLINE | ID: mdl-32120958
2.
Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform.
Sensors (Basel)
; 19(14)2019 Jul 13.
Artículo
en Inglés
| MEDLINE | ID: mdl-31337086
3.
Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height.
Front Plant Sci
; 13: 938216, 2022.
Artículo
en Inglés
| MEDLINE | ID: mdl-36092445
4.
Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices.
Front Plant Sci
; 13: 1012070, 2022.
Artículo
en Inglés
| MEDLINE | ID: mdl-36330259
5.
Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing.
Plant Methods
; 17(1): 51, 2021 May 17.
Artículo
en Inglés
| MEDLINE | ID: mdl-34001195
6.
A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages.
Plant Methods
; 16: 104, 2020.
Artículo
en Inglés
| MEDLINE | ID: mdl-32765637
7.
A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks.
Front Plant Sci
; 11: 402, 2020.
Artículo
en Inglés
| MEDLINE | ID: mdl-32351523
8.
A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique.
Front Plant Sci
; 11: 559, 2020.
Artículo
en Inglés
| MEDLINE | ID: mdl-32582225
9.
Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM.
Front Plant Sci
; 9: 1024, 2018.
Artículo
en Inglés
| MEDLINE | ID: mdl-30057587
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