TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture
Predicting crop yield is essential for managing agricultural production and addressing sustainability in the face of climate change. Rising temperatures and extreme weather events coupled with agriculture’s contribution to greenhouse gas emissions underscore the need for data-driven solutions. This...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Food & Agriculture |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2025.2529369 |
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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 | Predicting crop yield is essential for managing agricultural production and addressing sustainability in the face of climate change. Rising temperatures and extreme weather events coupled with agriculture’s contribution to greenhouse gas emissions underscore the need for data-driven solutions. This paper proposes a crop production prediction system using an Artificial Neural Network (ANN)-based TabNet model trained on extensive crop datasets. The model delivers actionable insights into optimal crop rotation strategies, particularly suited to Saudi Arabia’s agricultural conditions. TabNet demonstrated outperform prior models by achieving R2 scores of 0.9799 on Saudi Arabia’s Crop Yield dataset and 0.9850 on a benchmark dataset. The recommendations aim to enhance crop productivity, improve soil health, and mitigate climate change effects. By integrating ANN into agricultural decision-making, this approach supports sustainable farming practices and climate resilience. |
| format | Article |
| id | doaj-art-14df43eaad6049a28dace39b6283459f |
| institution | Kabale University |
| issn | 2331-1932 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj-art-14df43eaad6049a28dace39b6283459f2025-08-20T03:50:31ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322025-12-0111110.1080/23311932.2025.2529369TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agricultureMohammad 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, Albury, AustraliaDepartment of Computer Science and Engineering, School of Science and Engineering, Southeast University, Dhaka, BangladeshPredicting crop yield is essential for managing agricultural production and addressing sustainability in the face of climate change. Rising temperatures and extreme weather events coupled with agriculture’s contribution to greenhouse gas emissions underscore the need for data-driven solutions. This paper proposes a crop production prediction system using an Artificial Neural Network (ANN)-based TabNet model trained on extensive crop datasets. The model delivers actionable insights into optimal crop rotation strategies, particularly suited to Saudi Arabia’s agricultural conditions. TabNet demonstrated outperform prior models by achieving R2 scores of 0.9799 on Saudi Arabia’s Crop Yield dataset and 0.9850 on a benchmark dataset. The recommendations aim to enhance crop productivity, improve soil health, and mitigate climate change effects. By integrating ANN into agricultural decision-making, this approach supports sustainable farming practices and climate resilience.https://www.tandfonline.com/doi/10.1080/23311932.2025.2529369Crop yield predictionclimate changesustainabilityeconomic efficiencyartificial neural networkTabNet |
| spellingShingle | Mohammad M. Islam Majed Alharthi Rotana S. Alkadi Rafiqul Islam Abdul Kadar Muhammad Masum TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture Cogent Food & Agriculture Crop yield prediction climate change sustainability economic efficiency artificial neural network TabNet |
| title | TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture |
| title_full | TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture |
| title_fullStr | TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture |
| title_full_unstemmed | TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture |
| title_short | TabNet for crop yield prediction: promoting sustainability and climate resilience in Saudi Arabian agriculture |
| title_sort | tabnet for crop yield prediction promoting sustainability and climate resilience in saudi arabian agriculture |
| topic | Crop yield prediction climate change sustainability economic efficiency artificial neural network TabNet |
| url | https://www.tandfonline.com/doi/10.1080/23311932.2025.2529369 |
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