A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction

Bryan Wei Rong Ng; Joo Kiat Ng; Mohammed Ayoub Juman; Veera Ragavan; Ariel Jia Jin Leong

doi:10.1109/AGRETA61912.2024.10948892

A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction

Bryan Wei Rong Ng, Joo Kiat Ng, Mohammed Ayoub Juman, Veera Ragavan, Ariel Jia Jin Leong

Malaysia School of Engineering

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

Abstract

This paper addresses the research gap in precision agriculture by focusing on crop quality assessment and data interpretation. Through the use of data-driven methods and time series forecasting, a Cyber- Physical System based vertical farming system is proposed, with the integration of actuators and sensors. In combination with control and automation, the system allows for precise monitoring and adjustment of environmental parameters, optimizing resource utilization and increasing crop yield. Time series-based prediction using Decision Trees and Random Forest Regression resulted in a Mean Absolute Percentage Error (MAPE) of 31.42 % on average for crop weight prediction, which was further enhanced by using ensemble learning to create a stacked model with a MAPE of 8.30%. The outcomes of this research contribute to the advancement of precision agriculture practices and provide valuable insights for future applications in vertical farming automation.

Original language	English
Title of host publication	2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications
Subtitle of host publication	Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024
Editors	Syed Farid Syed Adnan
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	73-78
Number of pages	6
ISBN (Electronic)	9798350373660
ISBN (Print)	9798350373677
DOIs	https://doi.org/10.1109/AGRETA61912.2024.10948892
Publication status	Published - 2024
Event	IEEE International Conference on Agrosystem Engineering, Technology and Applications 2024 - Hybrid, Kuala Lumpur, Malaysia Duration: 7 Sept 2024 → 7 Sept 2024 https://ieeexplore.ieee.org/xpl/conhome/10948986/proceeding (Proceedings) https://web.archive.org/web/20241006160905/http://agreta.org/ (Website)

Conference

Conference	IEEE International Conference on Agrosystem Engineering, Technology and Applications 2024
Abbreviated title	AGRETA 2024
Country/Territory	Malaysia
City	Hybrid, Kuala Lumpur
Period	7/09/24 → 7/09/24
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10948986/proceeding (Proceedings) https://web.archive.org/web/20241006160905/http://agreta.org/ (Website)

Keywords

Cyber-Physical Systems
Growth Monitoring
Plant Yield Prediction
Precision Agriculture
Vertical Farming

Access to Document

10.1109/AGRETA61912.2024.10948892

Cite this

Rong Ng, B. W., Ng, J. K., Juman, M. A., Ragavan, V., & Jin Leong, A. J. (2024). A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction. In S. Farid Syed Adnan (Ed.), 2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications: Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024 (pp. 73-78). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/AGRETA61912.2024.10948892

Rong Ng, Bryan Wei ; Ng, Joo Kiat ; Juman, Mohammed Ayoub et al. / A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction. 2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications: Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024. editor / Syed Farid Syed Adnan. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2024. pp. 73-78

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abstract = "This paper addresses the research gap in precision agriculture by focusing on crop quality assessment and data interpretation. Through the use of data-driven methods and time series forecasting, a Cyber- Physical System based vertical farming system is proposed, with the integration of actuators and sensors. In combination with control and automation, the system allows for precise monitoring and adjustment of environmental parameters, optimizing resource utilization and increasing crop yield. Time series-based prediction using Decision Trees and Random Forest Regression resulted in a Mean Absolute Percentage Error (MAPE) of 31.42 \% on average for crop weight prediction, which was further enhanced by using ensemble learning to create a stacked model with a MAPE of 8.30\%. The outcomes of this research contribute to the advancement of precision agriculture practices and provide valuable insights for future applications in vertical farming automation.",

keywords = "Cyber-Physical Systems, Growth Monitoring, Plant Yield Prediction, Precision Agriculture, Vertical Farming",

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doi = "10.1109/AGRETA61912.2024.10948892",

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Rong Ng, BW, Ng, JK, Juman, MA , Ragavan, V & Jin Leong, AJ 2024, A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction. in S Farid Syed Adnan (ed.), 2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications: Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 73-78, IEEE International Conference on Agrosystem Engineering, Technology and Applications 2024, Hybrid, Kuala Lumpur, Malaysia, 7/09/24. https://doi.org/10.1109/AGRETA61912.2024.10948892

A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction. / Rong Ng, Bryan Wei; Ng, Joo Kiat; Juman, Mohammed Ayoub et al.
2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications: Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024. ed. / Syed Farid Syed Adnan. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2024. p. 73-78.

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

TY - GEN

T1 - A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction

AU - Rong Ng, Bryan Wei

AU - Ng, Joo Kiat

AU - Juman, Mohammed Ayoub

AU - Ragavan, Veera

AU - Jin Leong, Ariel Jia

PY - 2024

Y1 - 2024

N2 - This paper addresses the research gap in precision agriculture by focusing on crop quality assessment and data interpretation. Through the use of data-driven methods and time series forecasting, a Cyber- Physical System based vertical farming system is proposed, with the integration of actuators and sensors. In combination with control and automation, the system allows for precise monitoring and adjustment of environmental parameters, optimizing resource utilization and increasing crop yield. Time series-based prediction using Decision Trees and Random Forest Regression resulted in a Mean Absolute Percentage Error (MAPE) of 31.42 % on average for crop weight prediction, which was further enhanced by using ensemble learning to create a stacked model with a MAPE of 8.30%. The outcomes of this research contribute to the advancement of precision agriculture practices and provide valuable insights for future applications in vertical farming automation.

AB - This paper addresses the research gap in precision agriculture by focusing on crop quality assessment and data interpretation. Through the use of data-driven methods and time series forecasting, a Cyber- Physical System based vertical farming system is proposed, with the integration of actuators and sensors. In combination with control and automation, the system allows for precise monitoring and adjustment of environmental parameters, optimizing resource utilization and increasing crop yield. Time series-based prediction using Decision Trees and Random Forest Regression resulted in a Mean Absolute Percentage Error (MAPE) of 31.42 % on average for crop weight prediction, which was further enhanced by using ensemble learning to create a stacked model with a MAPE of 8.30%. The outcomes of this research contribute to the advancement of precision agriculture practices and provide valuable insights for future applications in vertical farming automation.

KW - Cyber-Physical Systems

KW - Growth Monitoring

KW - Plant Yield Prediction

KW - Precision Agriculture

KW - Vertical Farming

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DO - 10.1109/AGRETA61912.2024.10948892

M3 - Conference Paper

AN - SCOPUS:105003533925

SN - 9798350373677

SP - 73

EP - 78

BT - 2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications

A2 - Farid Syed Adnan, Syed

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Agrosystem Engineering, Technology and Applications 2024

Y2 - 7 September 2024 through 7 September 2024

ER -

Rong Ng BW, Ng JK, Juman MA , Ragavan V, Jin Leong AJ. A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction. In Farid Syed Adnan S, editor, 2024 IEEE International Conference on Agrosystem Engineering, Technology and Applications: Integrating Smart Farming and Food Security for a Sustainable Future, AGRETA 2024. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2024. p. 73-78 doi: 10.1109/AGRETA61912.2024.10948892

A Cyber-Physical Precision Agriculture System for Plant Growth and Yield Prediction

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