These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
142 related articles for article (PubMed ID: 39182775)
1. From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change. Mahdizadeh Gharakhanlou N; Perez L Sci Total Environ; 2024 Nov; 951():175764. PubMed ID: 39182775 [TBL] [Abstract][Full Text] [Related]
2. Yield prediction for crops by gradient-based algorithms. Mahesh P; Soundrapandiyan R PLoS One; 2024; 19(8):e0291928. PubMed ID: 39186769 [TBL] [Abstract][Full Text] [Related]
3. Predicting rice phenology across China by integrating crop phenology model and machine learning. Zhang J; Lin X; Jiang C; Hu X; Liu B; Liu L; Xiao L; Zhu Y; Cao W; Tang L Sci Total Environ; 2024 Nov; 951():175585. PubMed ID: 39155002 [TBL] [Abstract][Full Text] [Related]
4. Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. Li Z; Ding L; Xu D Sci Total Environ; 2022 Apr; 815():152880. PubMed ID: 34998760 [TBL] [Abstract][Full Text] [Related]
5. Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach. Prodhan FA; Zhang J; Pangali Sharma TP; Nanzad L; Zhang D; Seka AM; Ahmed N; Hasan SS; Hoque MZ; Mohana HP Sci Total Environ; 2022 Feb; 807(Pt 3):151029. PubMed ID: 34673078 [TBL] [Abstract][Full Text] [Related]
6. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Shahhosseini M; Hu G; Huber I; Archontoulis SV Sci Rep; 2021 Jan; 11(1):1606. PubMed ID: 33452349 [TBL] [Abstract][Full Text] [Related]
7. Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa. Kephe PN; Mkuhlani S; Rusere F; Chemura A PLoS One; 2024; 19(5):e0301254. PubMed ID: 38713689 [TBL] [Abstract][Full Text] [Related]
8. Climate and disease: tackling coffee brown-eye spot with advanced forecasting models. de Oliveira Aparecido LE; de Lima RF; Torsoni GB; Lorençone JA; Lorençone PA; de Souza Rolim G J Sci Food Agric; 2024 Jul; 104(9):5442-5461. PubMed ID: 38349004 [TBL] [Abstract][Full Text] [Related]
9. Climate change impacts on crop yield, soil water balance and nitrate leaching in the semiarid and humid regions of Canada. He W; Yang JY; Qian B; Drury CF; Hoogenboom G; He P; Lapen D; Zhou W PLoS One; 2018; 13(11):e0207370. PubMed ID: 30444929 [TBL] [Abstract][Full Text] [Related]
10. Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea. Shi Y; Li L; Wu B; Zhang Y; Wang B; Niu W; He L; Jin N; Pan S; Tian H; Yu Q Sci Total Environ; 2024 Oct; 946():174227. PubMed ID: 38936710 [TBL] [Abstract][Full Text] [Related]
11. Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective. Gyamerah SA; Asare C; Agbi-Kaeser HO; Baffour-Ata F PLoS One; 2024; 19(6):e0305762. PubMed ID: 38917094 [TBL] [Abstract][Full Text] [Related]
12. Evaluation of crop water stress index of wheat by using machine learning models. Yadav A; Narakala LM; Upreti H; Das Singhal G Environ Monit Assess; 2024 Sep; 196(10):970. PubMed ID: 39312101 [TBL] [Abstract][Full Text] [Related]
13. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations. Hoffmann H; Zhao G; Asseng S; Bindi M; Biernath C; Constantin J; Coucheney E; Dechow R; Doro L; Eckersten H; Gaiser T; Grosz B; Heinlein F; Kassie BT; Kersebaum KC; Klein C; Kuhnert M; Lewan E; Moriondo M; Nendel C; Priesack E; Raynal H; Roggero PP; Rötter RP; Siebert S; Specka X; Tao F; Teixeira E; Trombi G; Wallach D; Weihermüller L; Yeluripati J; Ewert F PLoS One; 2016; 11(4):e0151782. PubMed ID: 27055028 [TBL] [Abstract][Full Text] [Related]
14. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Huang JC; Tsai YC; Wu PY; Lien YH; Chien CY; Kuo CF; Hung JF; Chen SC; Kuo CH Comput Methods Programs Biomed; 2020 Oct; 195():105536. PubMed ID: 32485511 [TBL] [Abstract][Full Text] [Related]
15. Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models. Mokhtar A; He H; Nabil M; Kouadri S; Salem A; Elbeltagi A Sci Rep; 2024 Jun; 14(1):14699. PubMed ID: 38926368 [TBL] [Abstract][Full Text] [Related]
16. Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. Battisti R; Sentelhas PC; Boote KJ Int J Biometeorol; 2018 May; 62(5):823-832. PubMed ID: 29196806 [TBL] [Abstract][Full Text] [Related]
17. Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta. Nguyen HD; Van CP; Nguyen TG; Dang DK; Pham TTN; Nguyen QH; Bui QT Environ Sci Pollut Res Int; 2023 Jun; 30(29):74340-74357. PubMed ID: 37204580 [TBL] [Abstract][Full Text] [Related]
18. Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning. Bhullar A; Nadeem K; Ali RA Sci Rep; 2023 Apr; 13(1):6823. PubMed ID: 37100875 [TBL] [Abstract][Full Text] [Related]
19. Impact of derived global weather data on simulated crop yields. van Wart J; Grassini P; Cassman KG Glob Chang Biol; 2013 Dec; 19(12):3822-34. PubMed ID: 23801639 [TBL] [Abstract][Full Text] [Related]
20. Random Forests for Global and Regional Crop Yield Predictions. Jeong JH; Resop JP; Mueller ND; Fleisher DH; Yun K; Butler EE; Timlin DJ; Shim KM; Gerber JS; Reddy VR; Kim SH PLoS One; 2016; 11(6):e0156571. PubMed ID: 27257967 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]