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Self-Organising and Self-Learning Model for Soybean Yield Prediction

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Self-Organising and Self-Learning Model for Soybean Yield Prediction. / Alghamdi, Mona; Angelov, Plamen; Rufino, Mariana et al.

2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019. ed. / Mohammad Alsmirat; Yaser Jararweh. IEEE, 2019. p. 441-446.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Alghamdi, M, Angelov, P, Rufino, M, Gimenez, R & Almeida Soares, E 2019, Self-Organising and Self-Learning Model for Soybean Yield Prediction. in M Alsmirat & Y Jararweh (eds), 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019. IEEE, pp. 441-446. https://doi.org/10.1109/SNAMS.2019.8931888

APA

Alghamdi, M., Angelov, P., Rufino, M., Gimenez, R., & Almeida Soares, E. (2019). Self-Organising and Self-Learning Model for Soybean Yield Prediction. In M. Alsmirat, & Y. Jararweh (Eds.), 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019 (pp. 441-446). IEEE. https://doi.org/10.1109/SNAMS.2019.8931888

Vancouver

Alghamdi M, Angelov P, Rufino M, Gimenez R, Almeida Soares E. Self-Organising and Self-Learning Model for Soybean Yield Prediction. In Alsmirat M, Jararweh Y, editors, 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019. IEEE. 2019. p. 441-446 doi: 10.1109/SNAMS.2019.8931888

Author

Alghamdi, Mona ; Angelov, Plamen ; Rufino, Mariana et al. / Self-Organising and Self-Learning Model for Soybean Yield Prediction. 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019. editor / Mohammad Alsmirat ; Yaser Jararweh. IEEE, 2019. pp. 441-446

Bibtex

@inproceedings{9d6c5e1ef7384c6ea05e6c6dc79e5287,
title = "Self-Organising and Self-Learning Model for Soybean Yield Prediction",
abstract = "Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods. ",
author = "Mona Alghamdi and Plamen Angelov and Mariana Rufino and Raul Gimenez and {Almeida Soares}, Eduardo",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2019",
month = dec,
day = "16",
doi = "10.1109/SNAMS.2019.8931888",
language = "English",
isbn = "9781728129471",
pages = "441--446",
editor = "Mohammad Alsmirat and Yaser Jararweh",
booktitle = "2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Self-Organising and Self-Learning Model for Soybean Yield Prediction

AU - Alghamdi, Mona

AU - Angelov, Plamen

AU - Rufino, Mariana

AU - Gimenez, Raul

AU - Almeida Soares, Eduardo

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/12/16

Y1 - 2019/12/16

N2 - Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods.

AB - Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods.

U2 - 10.1109/SNAMS.2019.8931888

DO - 10.1109/SNAMS.2019.8931888

M3 - Conference contribution/Paper

SN - 9781728129471

SP - 441

EP - 446

BT - 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019

A2 - Alsmirat, Mohammad

A2 - Jararweh, Yaser

PB - IEEE

ER -