128 related articles for article (PubMed ID: 38521920)
1. Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.).
Zhang X; Xu H; She Y; Hu C; Zhu T; Wang L; Wu L; You C; Ke J; Zhang Q; He H
Plant Methods; 2024 Mar; 20(1):48. PubMed ID: 38521920
[TBL] [Abstract][Full Text] [Related]
2. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.
Zhang J; Zhang W; Xiong S; Song Z; Tian W; Shi L; Ma X
Plant Methods; 2021 Mar; 17(1):34. PubMed ID: 33789711
[TBL] [Abstract][Full Text] [Related]
3. Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.
Zununjan Z; Turghan MA; Sattar M; Kasim N; Emin B; Abliz A
Plant Methods; 2024 Jun; 20(1):97. PubMed ID: 38909230
[TBL] [Abstract][Full Text] [Related]
4. [Retrieval of leaf water content of winter wheat from canopy hyperspectral data using partial least square regression].
Wang YY; Li GC; Zhang LJ; Fan JL
Guang Pu Xue Yu Guang Pu Fen Xi; 2010 Apr; 30(4):1070-4. PubMed ID: 20545164
[TBL] [Abstract][Full Text] [Related]
5. Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.
Das B; Sahoo RN; Pargal S; Krishna G; Verma R; Viswanathan C; Sehgal VK; Gupta VK
Spectrochim Acta A Mol Biomol Spectrosc; 2021 Feb; 247():119104. PubMed ID: 33161273
[TBL] [Abstract][Full Text] [Related]
6. [Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus].
Li YD; Ye C; Cao ZS; Sun BF; Shu SF; Huang JB; Tian YC; He Y
Ying Yong Sheng Tai Xue Bao; 2020 Sep; 31(9):3040-3050. PubMed ID: 33345505
[TBL] [Abstract][Full Text] [Related]
7. Based on machine learning algorithms for estimating leaf phosphorus concentration of rice using optimized spectral indices and continuous wavelet transform.
Zhang Y; Wang T; Li Z; Wang T; Cao N
Front Plant Sci; 2023; 14():1185915. PubMed ID: 37304713
[TBL] [Abstract][Full Text] [Related]
8. Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.
Tagliabue G; Boschetti M; Bramati G; Candiani G; Colombo R; Nutini F; Pompilio L; Rivera-Caicedo JP; Rossi M; Rossini M; Verrelst J; Panigada C
ISPRS J Photogramm Remote Sens; 2022 May; 187():362-377. PubMed ID: 36093126
[TBL] [Abstract][Full Text] [Related]
9. Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices.
Sun H; Feng M; Xiao L; Yang W; Wang C; Jia X; Zhao Y; Zhao C; Muhammad SK; Li D
PLoS One; 2019; 14(6):e0216890. PubMed ID: 31181067
[TBL] [Abstract][Full Text] [Related]
10. Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation.
Zhang C; Liu J; Shang J; Cai H
Sci Total Environ; 2018 Aug; 631-632():677-687. PubMed ID: 29539596
[TBL] [Abstract][Full Text] [Related]
11. Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).
Liu X; Ferguson RB; Zheng H; Cao Q; Tian Y; Cao W; Zhu Y
Sensors (Basel); 2017 Mar; 17(4):. PubMed ID: 28338637
[TBL] [Abstract][Full Text] [Related]
12. Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data.
Tunca E; Köksal ES; Öztürk E; Akay H; Çetin Taner S
Environ Monit Assess; 2023 Jun; 195(7):877. PubMed ID: 37353582
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data.
Wang Z; Ma Y; Chen P; Yang Y; Fu H; Yang F; Raza MA; Guo C; Shu C; Sun Y; Yang Z; Chen Z; Ma J
Front Plant Sci; 2022; 13():903643. PubMed ID: 35712565
[TBL] [Abstract][Full Text] [Related]
15. Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters.
Shi X; Song J; Wang H; Lv X; Tian T; Wang J; Li W; Zhong M; Jiang M
Front Plant Sci; 2023; 14():1171594. PubMed ID: 37469774
[TBL] [Abstract][Full Text] [Related]
16. [Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].
Gao L; Li CC; Wang BS; Yang Gui-jun ; Wang L; Fu K
Ying Yong Sheng Tai Xue Bao; 2016 Jan; 27(1):191-200. PubMed ID: 27228609
[TBL] [Abstract][Full Text] [Related]
17. Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice.
Din M; Ming J; Hussain S; Ata-Ul-Karim ST; Rashid M; Tahir MN; Hua S; Wang S
Front Plant Sci; 2018; 9():1883. PubMed ID: 30697219
[TBL] [Abstract][Full Text] [Related]
18. Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.
Ko J; Shin T; Kang J; Baek J; Sang WG
Front Plant Sci; 2024; 15():1320969. PubMed ID: 38410726
[TBL] [Abstract][Full Text] [Related]
19. Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images.
Lin F; Guo S; Tan C; Zhou X; Zhang D
Sensors (Basel); 2020 Nov; 20(21):. PubMed ID: 33147714
[TBL] [Abstract][Full Text] [Related]
20. Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.
Li S; Jin Z; Bai J; Xiang S; Xu C; Yu F
Front Plant Sci; 2024; 15():1405239. PubMed ID: 38911973
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]