Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia
This study aimed to explain the crop yield prediction system as a way to address the challenges posed by global warming and climate change in Saudi Arabia, while also taking into account socio-economic factors. Machine learning models were trained using crop yield prediction data to provide recommen...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-10-01
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| Series: | AIMS Agriculture and Food |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/agrfood.2024053 |
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| author | Mohammad M. Islam Majed Alharthi Rotana S. Alkadi Rafiqul Islam Abdul Kadar Muhammad Masum |
| author_facet | Mohammad M. Islam Majed Alharthi Rotana S. Alkadi Rafiqul Islam Abdul Kadar Muhammad Masum |
| author_sort | Mohammad M. Islam |
| collection | DOAJ |
| description | This study aimed to explain the crop yield prediction system as a way to address the challenges posed by global warming and climate change in Saudi Arabia, while also taking into account socio-economic factors. Machine learning models were trained using crop yield prediction data to provide recommendations for future crop production. Climate change poses significant challenges, with rising temperatures and extreme weather events being increasingly evident. Agriculture, contributing 14% of greenhouse gas emissions, plays a crucial role in exacerbating this issue. This study introduced a crop yield prediction system leveraging machine learning models trained on comprehensive datasets. Recommendations derived from these models offer insights into optimal crop rotation strategies, particularly relevant for regions like the Kingdom of Saudi Arabia. Collaboration between farmers and governments, informed by data-driven approaches, is crucial in this endeavor. Utilizing a customized dataset, this study analyzed a machine learning model performance and identified optimal hyperparameters. XGBoost ensemble emerged as the top performer with an R2 score of 0.9745, showcasing its potential to advance crop yield prediction capabilities. By integrating machine learning into agricultural decision-making processes, stakeholders aim to enhance crop production and soil health and contribute to climate change mitigation efforts. This collaborative effort represents a significant step toward sustainable agriculture and climate resilience in Saudi Arabia. |
| format | Article |
| id | doaj-art-344aa0f412fc40d1aa579d32fdd3542f |
| institution | Kabale University |
| issn | 2471-2086 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Agriculture and Food |
| spelling | doaj-art-344aa0f412fc40d1aa579d32fdd3542f2025-01-24T01:24:30ZengAIMS PressAIMS Agriculture and Food2471-20862024-10-0194980100310.3934/agrfood.2024053Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi ArabiaMohammad M. Islam0Majed Alharthi1Rotana S. Alkadi2Rafiqul Islam3Abdul Kadar Muhammad Masum4Department of Finance, College of Business, King Abdulaziz University, Rabigh, Saudi ArabiaDepartment of Finance, College of Business, King Abdulaziz University, Rabigh, Saudi ArabiaDepartment of Finance, College of Business, King Abdulaziz University, Rabigh, Saudi ArabiaSchool of computing mathematics and engineering, Charles Sturt University, AustraliaDepartment of Software Engineering, Daffodil International University, BangladeshThis study aimed to explain the crop yield prediction system as a way to address the challenges posed by global warming and climate change in Saudi Arabia, while also taking into account socio-economic factors. Machine learning models were trained using crop yield prediction data to provide recommendations for future crop production. Climate change poses significant challenges, with rising temperatures and extreme weather events being increasingly evident. Agriculture, contributing 14% of greenhouse gas emissions, plays a crucial role in exacerbating this issue. This study introduced a crop yield prediction system leveraging machine learning models trained on comprehensive datasets. Recommendations derived from these models offer insights into optimal crop rotation strategies, particularly relevant for regions like the Kingdom of Saudi Arabia. Collaboration between farmers and governments, informed by data-driven approaches, is crucial in this endeavor. Utilizing a customized dataset, this study analyzed a machine learning model performance and identified optimal hyperparameters. XGBoost ensemble emerged as the top performer with an R2 score of 0.9745, showcasing its potential to advance crop yield prediction capabilities. By integrating machine learning into agricultural decision-making processes, stakeholders aim to enhance crop production and soil health and contribute to climate change mitigation efforts. This collaborative effort represents a significant step toward sustainable agriculture and climate resilience in Saudi Arabia.https://www.aimspress.com/article/doi/10.3934/agrfood.2024053climate changemachine learningprecision agriculturesaudi arabiasustainabilitysustainable finance |
| spellingShingle | Mohammad M. Islam Majed Alharthi Rotana S. Alkadi Rafiqul Islam Abdul Kadar Muhammad Masum Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia AIMS Agriculture and Food climate change machine learning precision agriculture saudi arabia sustainability sustainable finance |
| title | Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia |
| title_full | Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia |
| title_fullStr | Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia |
| title_full_unstemmed | Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia |
| title_short | Crop yield prediction through machine learning: A path towards sustainable agriculture and climate resilience in Saudi Arabia |
| title_sort | crop yield prediction through machine learning a path towards sustainable agriculture and climate resilience in saudi arabia |
| topic | climate change machine learning precision agriculture saudi arabia sustainability sustainable finance |
| url | https://www.aimspress.com/article/doi/10.3934/agrfood.2024053 |
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