Wheat Yield Estimation and Predication Via Machine Learning

Mukesh Singh Boori; Komal Choudhary; Rustam Paringer; Alexander Kupriyanov; Youngwook Kim

doi:10.1109/ITNT57377.2023.10139117

Wheat Yield Estimation and Predication Via Machine Learning

Mukesh Singh Boori, Komal Choudhary, Rustam Paringer, Alexander Kupriyanov, Youngwook Kim

Department of Biology

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

Abstract

A precise wheat yield estimation and prediction are significant for food safety and security purposes of a region or a country, which provide societal peace and sustainable development. Earlier methods for wheat yield prediction are time-consuming, site-specific, and expensive, require more manpower, and delay results with numerous errors and uncertainty. This research work uses numerous heterogeneous data in machine learning via linear regression (LR), decision tree (DT), and random forest (RF) regression by python for accurate wheat yield estimation and prediction at 10m resolution. In a comparison of all three regressions, RF shows the highest accuracy with R2: 98, and RMSE: 1.40, which is also increasing from seedling to harvest growth stage. This research work provides precision agriculture for the sustainable development of a region or a country.

Original language	English
Title of host publication	Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350397338
DOIs	https://doi.org/10.1109/ITNT57377.2023.10139117
Publication status	Published - 2023
Event	9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023 - Virtual, Online, Russian Federation Duration: Apr 17 2023 → Apr 21 2023

Publication series

Name	Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023

Conference

Conference	9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023
Country/Territory	Russian Federation
City	Virtual, Online
Period	4/17/23 → 4/21/23

Keywords

Machine Learning
Regression
Sentinel-2
Spectral-catalogs
Wheat Yield Estimation and Prediction

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing
Information Systems and Management
Health Informatics

Access to Document

10.1109/ITNT57377.2023.10139117

Cite this

Boori, M. S., Choudhary, K., Paringer, R., Kupriyanov, A., & Kim, Y. (2023). Wheat Yield Estimation and Predication Via Machine Learning. In Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023 (Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNT57377.2023.10139117

Wheat Yield Estimation and Predication Via Machine Learning. / Boori, Mukesh Singh; Choudhary, Komal; Paringer, Rustam et al.
Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023).

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

Boori, MS, Choudhary, K, Paringer, R, Kupriyanov, A & Kim, Y 2023, Wheat Yield Estimation and Predication Via Machine Learning. in Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023. Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023, Virtual, Online, Russian Federation, 4/17/23. https://doi.org/10.1109/ITNT57377.2023.10139117

Boori MS, Choudhary K, Paringer R, Kupriyanov A, Kim Y. Wheat Yield Estimation and Predication Via Machine Learning. In Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023). doi: 10.1109/ITNT57377.2023.10139117

Boori, Mukesh Singh ; Choudhary, Komal ; Paringer, Rustam et al. / Wheat Yield Estimation and Predication Via Machine Learning. Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - 9th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2023).

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abstract = "A precise wheat yield estimation and prediction are significant for food safety and security purposes of a region or a country, which provide societal peace and sustainable development. Earlier methods for wheat yield prediction are time-consuming, site-specific, and expensive, require more manpower, and delay results with numerous errors and uncertainty. This research work uses numerous heterogeneous data in machine learning via linear regression (LR), decision tree (DT), and random forest (RF) regression by python for accurate wheat yield estimation and prediction at 10m resolution. In a comparison of all three regressions, RF shows the highest accuracy with R2: 98, and RMSE: 1.40, which is also increasing from seedling to harvest growth stage. This research work provides precision agriculture for the sustainable development of a region or a country.",

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AB - A precise wheat yield estimation and prediction are significant for food safety and security purposes of a region or a country, which provide societal peace and sustainable development. Earlier methods for wheat yield prediction are time-consuming, site-specific, and expensive, require more manpower, and delay results with numerous errors and uncertainty. This research work uses numerous heterogeneous data in machine learning via linear regression (LR), decision tree (DT), and random forest (RF) regression by python for accurate wheat yield estimation and prediction at 10m resolution. In a comparison of all three regressions, RF shows the highest accuracy with R2: 98, and RMSE: 1.40, which is also increasing from seedling to harvest growth stage. This research work provides precision agriculture for the sustainable development of a region or a country.

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Wheat Yield Estimation and Predication Via Machine Learning

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