Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models
The rising global demand for oil palm emphasizes the importance of accurate oil palm yield predictions. This predictive capability is critical for effective crop management, supply chain optimization, and sustainable farming practices. However, the oil palm sector faces challenges in yield projectio...
| Egile Nagusiak: | , , , , , , , , , |
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| Formatua: | Artikulua |
| Hizkuntza: | English |
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Elsevier
2024
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| Sarrera elektronikoa: | http://psasir.upm.edu.my/id/eprint/112419/1/112419.pdf |
| _version_ | 1825940127887654912 |
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| author | Jamshidi, Ehsan Jolous Yusup, Yusri Hooy, Chee Wooi Kamaruddin, Mohamad Anuar Mat Hassan, Hasnuri Muhammad, Syahidah Akmal Mohd Shafri, Helmi Zulhaidi Then, Kek Hoe Norizan, Mohd Shahkhirat Tan, Choon Chek |
| author_facet | Jamshidi, Ehsan Jolous Yusup, Yusri Hooy, Chee Wooi Kamaruddin, Mohamad Anuar Mat Hassan, Hasnuri Muhammad, Syahidah Akmal Mohd Shafri, Helmi Zulhaidi Then, Kek Hoe Norizan, Mohd Shahkhirat Tan, Choon Chek |
| author_sort | Jamshidi, Ehsan Jolous |
| collection | UPM |
| description | The rising global demand for oil palm emphasizes the importance of accurate oil palm yield predictions. This predictive capability is critical for effective crop management, supply chain optimization, and sustainable farming practices. However, the oil palm sector faces challenges in yield projection, stressing a noteworthy gap in the application and evaluation of modern machine learning and deep learning technologies. Our study addressed this gap by systematically evaluating 17 machine and deep learning models in predicting oil palm yield, incorporating various agronomic parameters, e.g., soil composition, climatic conditions, plant age, and farming techniques. This holistic approach enhances the application of machine and deep learning in agriculture. Using the feature selection technique and a maximum depth of 32 and 1000 estimators, the Extra Trees Regressor exhibited positive performance, i.e., MSE = 860.36 and an R2 = 0.65, and stands out among the 17 models evaluated. Our findings also showed that incorporating a comprehensive agronomic dataset is critical to accurate yield prediction. Hence, this model and approach have the potential to be a robust decision-making tool for agronomists and farmers in the oil palm industry, setting the stage for future innovations in sustainable agriculture practices. |
| first_indexed | 2024-12-09T02:23:55Z |
| format | Article |
| id | upm.eprints-112419 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-12-09T02:23:55Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1124192024-09-25T07:44:42Z http://psasir.upm.edu.my/id/eprint/112419/ Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models Jamshidi, Ehsan Jolous Yusup, Yusri Hooy, Chee Wooi Kamaruddin, Mohamad Anuar Mat Hassan, Hasnuri Muhammad, Syahidah Akmal Mohd Shafri, Helmi Zulhaidi Then, Kek Hoe Norizan, Mohd Shahkhirat Tan, Choon Chek The rising global demand for oil palm emphasizes the importance of accurate oil palm yield predictions. This predictive capability is critical for effective crop management, supply chain optimization, and sustainable farming practices. However, the oil palm sector faces challenges in yield projection, stressing a noteworthy gap in the application and evaluation of modern machine learning and deep learning technologies. Our study addressed this gap by systematically evaluating 17 machine and deep learning models in predicting oil palm yield, incorporating various agronomic parameters, e.g., soil composition, climatic conditions, plant age, and farming techniques. This holistic approach enhances the application of machine and deep learning in agriculture. Using the feature selection technique and a maximum depth of 32 and 1000 estimators, the Extra Trees Regressor exhibited positive performance, i.e., MSE = 860.36 and an R2 = 0.65, and stands out among the 17 models evaluated. Our findings also showed that incorporating a comprehensive agronomic dataset is critical to accurate yield prediction. Hence, this model and approach have the potential to be a robust decision-making tool for agronomists and farmers in the oil palm industry, setting the stage for future innovations in sustainable agriculture practices. Elsevier 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/112419/1/112419.pdf Jamshidi, Ehsan Jolous and Yusup, Yusri and Hooy, Chee Wooi and Kamaruddin, Mohamad Anuar and Mat Hassan, Hasnuri and Muhammad, Syahidah Akmal and Mohd Shafri, Helmi Zulhaidi and Then, Kek Hoe and Norizan, Mohd Shahkhirat and Tan, Choon Chek (2024) Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models. Ecological Informatics, 81. art. no. 102595. pp. 1-12. ISSN 1574-9541 https://www.sciencedirect.com/science/article/pii/S1574954124001377 10.1016/j.ecoinf.2024.102595 |
| spellingShingle | Jamshidi, Ehsan Jolous Yusup, Yusri Hooy, Chee Wooi Kamaruddin, Mohamad Anuar Mat Hassan, Hasnuri Muhammad, Syahidah Akmal Mohd Shafri, Helmi Zulhaidi Then, Kek Hoe Norizan, Mohd Shahkhirat Tan, Choon Chek Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title | Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title_full | Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title_fullStr | Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title_full_unstemmed | Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title_short | Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| title_sort | predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models |
| url | http://psasir.upm.edu.my/id/eprint/112419/1/112419.pdf |
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