From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries

Saniya Nangia; Thilanka Munasinghe; Heidi Tubbs; Assaf Anyamba

doi:10.1109/BigData59044.2023.10386225

From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries

Saniya Nangia, Thilanka Munasinghe, Heidi Tubbs, Assaf Anyamba

Remote Sensing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work-in-progress study, we aim to determine the predictors of corn production in Iowa (United States), Heilongjiang (China), Mato Grosso (Brazil), Cordoba (Argentina), and Poltava Oblast (Ukraine). The effectiveness of predicting annual corn production using precipitation and land surface temperature (LST) values obtained from the Earth Engine tool, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) values obtained from the National Aeronautics and Space Administration (NASA) Global Inventory Monitoring and Modelling Studies (GIMMS) Global Agricultural Monitoring System, is examined. A comparison is conducted between multiple linear regression, ridge regression, and lasso regression models. The highest adjusted R² values are found in order to identify the optimal model in which the corn yield variance is explained by a combination of variables including the year, NDVI sample and anomaly values, precipitation, and LST. The results show that corn production values in Iowa, Heilongjiang, Mato Grosso, Cordoba, and Poltava Oblast can best be predicted using lasso regression models with adjusted R² values of 0.841, 0.933, 0.847, 0.854, and 0.860 respectively.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3479-3488
Number of pages	10
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386225
State	Published - 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: Dec 15 2023 → Dec 18 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	12/15/23 → 12/18/23

Funding

Code repository and the enlarged versions of the images and plots are available at: https://github.com/saniyanangia/Crop_Production ACKNOWLEDGMENT We acknowledge USDA for publicly sharing the corn production data and the publicly available crop production maps (Fig. 3). NDVI data was provided by the Global Inventory Modeling and Mapping Studies (GIMMS) Group Global Agricultural Monitoring (GLAM) System (https://glam1.gsfc.nasa.gov/) funded through an interagency agreement grant between USDA/FAS/OGA and NASA/GSFC. Regional crop area maps (Fig. 2) were also obtained from this source. Other data sets including temperature and precipitation data were obtained from other NASA data portals. Dr. Assaf Anyamba and Heidi Tubbs’s participation in this research was enabled through Group on Earth Observations (GEO) Health Community of Practice (CoP) activities for student engagement under the direction of Thilanka Munasinghe and Dr. Assaf Anyamba (http://www.geohealthcop.org/).

Funders	Funder number
OGA
National Aeronautics and Space Administration
U.S. Department of Agriculture
Georgia Student Finance Commission
Forskningsrådet för Arbetsliv och Socialvetenskap

Keywords

Agriculture
Earth Observational Data
Machine Learning
NDVI
Yield estimation

Access to Document

10.1109/BigData59044.2023.10386225

Cite this

Nangia, S., Munasinghe, T., Tubbs, H., & Anyamba, A. (2023). From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 3479-3488). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386225

Nangia, Saniya ; Munasinghe, Thilanka ; Tubbs, Heidi et al. / From Satellites to Fields : Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3479-3488 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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title = "From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries",

abstract = "In this work-in-progress study, we aim to determine the predictors of corn production in Iowa (United States), Heilongjiang (China), Mato Grosso (Brazil), Cordoba (Argentina), and Poltava Oblast (Ukraine). The effectiveness of predicting annual corn production using precipitation and land surface temperature (LST) values obtained from the Earth Engine tool, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) values obtained from the National Aeronautics and Space Administration (NASA) Global Inventory Monitoring and Modelling Studies (GIMMS) Global Agricultural Monitoring System, is examined. A comparison is conducted between multiple linear regression, ridge regression, and lasso regression models. The highest adjusted R2 values are found in order to identify the optimal model in which the corn yield variance is explained by a combination of variables including the year, NDVI sample and anomaly values, precipitation, and LST. The results show that corn production values in Iowa, Heilongjiang, Mato Grosso, Cordoba, and Poltava Oblast can best be predicted using lasso regression models with adjusted R2 values of 0.841, 0.933, 0.847, 0.854, and 0.860 respectively.",

keywords = "Agriculture, Earth Observational Data, Machine Learning, NDVI, Yield estimation",

author = "Saniya Nangia and Thilanka Munasinghe and Heidi Tubbs and Assaf Anyamba",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.1109/BigData59044.2023.10386225",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

}

Nangia, S, Munasinghe, T, Tubbs, H & Anyamba, A 2023, From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3479-3488, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 12/15/23. https://doi.org/10.1109/BigData59044.2023.10386225

From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries. / Nangia, Saniya; Munasinghe, Thilanka; Tubbs, Heidi et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3479-3488 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - From Satellites to Fields

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AU - Nangia, Saniya

AU - Munasinghe, Thilanka

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AU - Anyamba, Assaf

PY - 2023

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N2 - In this work-in-progress study, we aim to determine the predictors of corn production in Iowa (United States), Heilongjiang (China), Mato Grosso (Brazil), Cordoba (Argentina), and Poltava Oblast (Ukraine). The effectiveness of predicting annual corn production using precipitation and land surface temperature (LST) values obtained from the Earth Engine tool, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) values obtained from the National Aeronautics and Space Administration (NASA) Global Inventory Monitoring and Modelling Studies (GIMMS) Global Agricultural Monitoring System, is examined. A comparison is conducted between multiple linear regression, ridge regression, and lasso regression models. The highest adjusted R2 values are found in order to identify the optimal model in which the corn yield variance is explained by a combination of variables including the year, NDVI sample and anomaly values, precipitation, and LST. The results show that corn production values in Iowa, Heilongjiang, Mato Grosso, Cordoba, and Poltava Oblast can best be predicted using lasso regression models with adjusted R2 values of 0.841, 0.933, 0.847, 0.854, and 0.860 respectively.

AB - In this work-in-progress study, we aim to determine the predictors of corn production in Iowa (United States), Heilongjiang (China), Mato Grosso (Brazil), Cordoba (Argentina), and Poltava Oblast (Ukraine). The effectiveness of predicting annual corn production using precipitation and land surface temperature (LST) values obtained from the Earth Engine tool, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) values obtained from the National Aeronautics and Space Administration (NASA) Global Inventory Monitoring and Modelling Studies (GIMMS) Global Agricultural Monitoring System, is examined. A comparison is conducted between multiple linear regression, ridge regression, and lasso regression models. The highest adjusted R2 values are found in order to identify the optimal model in which the corn yield variance is explained by a combination of variables including the year, NDVI sample and anomaly values, precipitation, and LST. The results show that corn production values in Iowa, Heilongjiang, Mato Grosso, Cordoba, and Poltava Oblast can best be predicted using lasso regression models with adjusted R2 values of 0.841, 0.933, 0.847, 0.854, and 0.860 respectively.

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T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

SP - 3479

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A2 - He, Jingrui

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PB - Institute of Electrical and Electronics Engineers Inc.

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Nangia S, Munasinghe T, Tubbs H, Anyamba A. From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3479-3488. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386225

From Satellites to Fields: Machine Learning Applications for Prediction of Corn Production Using NDVI, Precipitation and Land Surface Temperature for Large Producer Countries

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