319 related articles for article (PubMed ID: 35401643)
1. 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]
2. In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery.
Niu H; Peddagudreddygari JR; Bhandari M; Landivar JA; Bednarz CW; Duffield N
Sensors (Basel); 2024 Apr; 24(8):. PubMed ID: 38676047
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture.
Mazzia V; Comba L; Khaliq A; Chiaberge M; Gay P
Sensors (Basel); 2020 Apr; 20(9):. PubMed ID: 32365636
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery.
Zhou H; Yang J; Lou W; Sheng L; Li D; Hu H
Front Plant Sci; 2023; 14():1217448. PubMed ID: 37908835
[TBL] [Abstract][Full Text] [Related]
9. Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development.
Shafian S; Rajan N; Schnell R; Bagavathiannan M; Valasek J; Shi Y; Olsenholler J
PLoS One; 2018; 13(5):e0196605. PubMed ID: 29715311
[TBL] [Abstract][Full Text] [Related]
10. Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis.
Duan B; Fang S; Zhu R; Wu X; Wang S; Gong Y; Peng Y
Front Plant Sci; 2019; 10():204. PubMed ID: 30873194
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops.
Hu P; Chapman SC; Zheng B
Funct Plant Biol; 2021 Jul; 48(8):766-779. PubMed ID: 33663681
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Deep learning techniques to classify agricultural crops through UAV imagery: a review.
Bouguettaya A; Zarzour H; Kechida A; Taberkit AM
Neural Comput Appl; 2022; 34(12):9511-9536. PubMed ID: 35281624
[TBL] [Abstract][Full Text] [Related]
15. Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery.
Wang F; Wang F; Zhang Y; Hu J; Huang J; Xie J
Front Plant Sci; 2019; 10():453. PubMed ID: 31024607
[TBL] [Abstract][Full Text] [Related]
16. Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data.
Gebrehiwot A; Hashemi-Beni L; Thompson G; Kordjamshidi P; Langan TE
Sensors (Basel); 2019 Mar; 19(7):. PubMed ID: 30934695
[TBL] [Abstract][Full Text] [Related]
17. Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network.
Tang M; Sadowski DL; Peng C; Vougioukas SG; Klever B; Khalsa SDS; Brown PH; Jin Y
Front Plant Sci; 2023; 14():1070699. PubMed ID: 36875622
[TBL] [Abstract][Full Text] [Related]
18. Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice.
Zheng H; Cheng T; Li D; Yao X; Tian Y; Cao W; Zhu Y
Front Plant Sci; 2018; 9():936. PubMed ID: 30034405
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.
Huang H; Deng J; Lan Y; Yang A; Deng X; Zhang L
PLoS One; 2018; 13(4):e0196302. PubMed ID: 29698500
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]