Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024)

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Applying Random Forest for Optimal Crop Selection to Enhance Agricultural Decision-Making

Authors
Nurul Qomariyah1, *, Septafiansyah Dwi Putra1, Dian Ayu Afifah1, Agiska Ria Supriyatna1, Zuriati Zuriati1
1Internet Engineering Technology, Politeknik Negeri Lampung, Lampung, Indonesia
*Corresponding author. Email: nqomariyah@polinela.ac.id
Corresponding Author
Nurul Qomariyah
Available Online 25 December 2024.
DOI
10.2991/978-94-6463-620-8_6How to use a DOI?
Keywords
Random Forest; Crop Selection; Precision Agriculture
Abstract

This paper explores the application of the Random Forest algorithm to optimize crop selection in precision agriculture. By integrating IoT-based data collection with machine learning, the study develops a data-driven approach to recommend the most suitable crops based on key environmental and soil parameters. The model demonstrated high accuracy in predicting crop suitability, and feature importance analysis revealed that factors such as soil pH, rainfall, and temperature play a critical role in crop selection. However, the study did not involve real-world testing, which remains a limitation in assessing the model’s practical applicability. Challenges such as noisy datasets, digital infrastructure limitations, and the need for farmer training present significant hurdles to the widespread adoption of this technology. Future research should focus on real-world trials and the integration of hybrid models to enhance performance in diverse agricultural settings. This approach has the potential to support data-driven decision-making in agriculture, ultimately contributing to enhanced productivity and sustainability.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024)
Series
Advances in Engineering Research
Publication Date
25 December 2024
ISBN
978-94-6463-620-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-620-8_6How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nurul Qomariyah
AU  - Septafiansyah Dwi Putra
AU  - Dian Ayu Afifah
AU  - Agiska Ria Supriyatna
AU  - Zuriati Zuriati
PY  - 2024
DA  - 2024/12/25
TI  - Applying Random Forest for Optimal Crop Selection to Enhance Agricultural Decision-Making
BT  - Proceedings of the  7th International Conference on Applied Engineering (ICAE 2024)
PB  - Atlantis Press
SP  - 66
EP  - 77
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-620-8_6
DO  - 10.2991/978-94-6463-620-8_6
ID  - Qomariyah2024
ER  -