A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and re-gression alg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116070/ http://psasir.upm.edu.my/id/eprint/116070/1/116070.pdf |
| _version_ | 1848866921174269952 |
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| author | Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lai, Soon Lee Ghazvini, Anahita |
| author_facet | Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lai, Soon Lee Ghazvini, Anahita |
| author_sort | Mohd Sharef, Nurfadhlina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and re-gression algorithms to create a digital twin for farmers to augment the concentration and compo-sition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and en-vironmental conditions makes nutrient content analysis expensive and time-consuming. This pa-per presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the co-existence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. |
| first_indexed | 2025-11-15T14:28:17Z |
| format | Article |
| id | upm-116070 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:28:17Z |
| publishDate | 2024 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1160702025-03-19T02:57:56Z http://psasir.upm.edu.my/id/eprint/116070/ A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lai, Soon Lee Ghazvini, Anahita Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and re-gression algorithms to create a digital twin for farmers to augment the concentration and compo-sition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and en-vironmental conditions makes nutrient content analysis expensive and time-consuming. This pa-per presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the co-existence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. MDPI AG 2024-04-17 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/116070/1/116070.pdf Mohd Sharef, Nurfadhlina and Balasundram, Siva Kumar and Lai, Soon Lee and Ghazvini, Anahita (2024) A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms. Applied Sciences-Basel, 14 (8). art. no. 3383. pp. 1-24. ISSN 2076-3417 https://www.mdpi.com/2076-3417/14/8/3383 10.3390/app14083383 |
| spellingShingle | Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lai, Soon Lee Ghazvini, Anahita A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title | A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title_full | A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title_fullStr | A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title_full_unstemmed | A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title_short | A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| title_sort | concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms |
| url | http://psasir.upm.edu.my/id/eprint/116070/ http://psasir.upm.edu.my/id/eprint/116070/ http://psasir.upm.edu.my/id/eprint/116070/ http://psasir.upm.edu.my/id/eprint/116070/1/116070.pdf |