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.
188 related articles for article (PubMed ID: 36262130)
1. Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia. Al-Adhaileh MH; Aldhyani THH PeerJ Comput Sci; 2022; 8():e1104. PubMed ID: 36262130 [TBL] [Abstract][Full Text] [Related]
2. Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models. Soroush F; Ehteram M; Seifi A Environ Sci Pollut Res Int; 2023 Feb; 30(8):20887-20906. PubMed ID: 36261636 [TBL] [Abstract][Full Text] [Related]
3. Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems. Qader SH; Utazi CE; Priyatikanto R; Najmaddin P; Hama-Ali EO; Khwarahm NR; Tatem AJ; Dash J Sci Total Environ; 2023 Apr; 869():161716. PubMed ID: 36690106 [TBL] [Abstract][Full Text] [Related]
4. Modeling crop yield using NDVI-derived VGM metrics across different climatic regions in the USA. Shammi SA; Meng Q Int J Biometeorol; 2023 Jun; 67(6):1051-1062. PubMed ID: 37195358 [TBL] [Abstract][Full Text] [Related]
5. Predicting reservoir sedimentation using multilayer perceptron - Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia. Lukas P; Melesse AM; Kenea TT J Environ Manage; 2024 May; 359():121018. PubMed ID: 38714033 [TBL] [Abstract][Full Text] [Related]
6. Using machine learning for crop yield prediction in the past or the future. Morales A; Villalobos FJ Front Plant Sci; 2023; 14():1128388. PubMed ID: 37063228 [TBL] [Abstract][Full Text] [Related]
7. Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques. Wangwongchai A; Waqas M; Dechpichai P; Hlaing PT; Ahmad S; Humphries UW MethodsX; 2023 Dec; 11():102459. PubMed ID: 38023312 [TBL] [Abstract][Full Text] [Related]
8. Rainfall prediction using multiple inclusive models and large climate indices. Mohamadi S; Sheikh Khozani Z; Ehteram M; Ahmed AN; El-Shafie A Environ Sci Pollut Res Int; 2022 Dec; 29(56):85312-85349. PubMed ID: 35790639 [TBL] [Abstract][Full Text] [Related]
9. Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches. Mousavi SR; Jahandideh Mahjenabadi VA; Khoshru B; Rezaei M Front Plant Sci; 2023; 14():1309171. PubMed ID: 38264030 [TBL] [Abstract][Full Text] [Related]
10. Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment. Lashkari A; Salehnia N; Asadi S; Paymard P; Zare H; Bannayan M Int J Biometeorol; 2018 Aug; 62(8):1543-1556. PubMed ID: 29740702 [TBL] [Abstract][Full Text] [Related]
11. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Jeong S; Ko J; Yeom JM Sci Total Environ; 2022 Jan; 802():149726. PubMed ID: 34464811 [TBL] [Abstract][Full Text] [Related]
12. Relationship between desert locust, Schistocerca gregaria (Forskål), infestation, environmental factors and control measures in Gazan and Makkah Regions, Saudi Arabia. Al-Ajlan AM Pak J Biol Sci; 2007 Oct; 10(20):3507-15. PubMed ID: 19093456 [TBL] [Abstract][Full Text] [Related]
13. Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. Das B; Nair B; Reddy VK; Venkatesh P Int J Biometeorol; 2018 Oct; 62(10):1809-1822. PubMed ID: 30043218 [TBL] [Abstract][Full Text] [Related]
14. Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Gong L; Yu M; Jiang S; Cutsuridis V; Pearson S Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34283083 [TBL] [Abstract][Full Text] [Related]
15. Prediction of Potato Crop Yield Using Precision Agriculture Techniques. Al-Gaadi KA; Hassaballa AA; Tola E; Kayad AG; Madugundu R; Alblewi B; Assiri F PLoS One; 2016; 11(9):e0162219. PubMed ID: 27611577 [TBL] [Abstract][Full Text] [Related]
16. Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat. Ren A; Jiang D; Kang M; Wu J; Xiao F; Hou P; Fu X Plant Methods; 2022 Jun; 18(1):77. PubMed ID: 35672714 [TBL] [Abstract][Full Text] [Related]
17. Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis. Fooladi M; Golmohammadi MH; Safavi HR; Singh VP J Environ Manage; 2021 Nov; 297():113283. PubMed ID: 34280857 [TBL] [Abstract][Full Text] [Related]
18. 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 Aug; 951():175585. PubMed ID: 39155002 [TBL] [Abstract][Full Text] [Related]
19. Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation. Hasan M; Marjan MA; Uddin MP; Afjal M; Kardy S; Ma S; Nam Y Front Plant Sci; 2023; 14():1234555. PubMed ID: 37636091 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]