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 regression algo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/111545/ http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf |
| _version_ | 1848865716187430912 |
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| author | Ghazvini, Anahita Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lee, Lai Soon |
| author_facet | Ghazvini, Anahita Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lee, Lai Soon |
| author_sort | Ghazvini, Anahita |
| 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 regression algorithms to create a digital twin for farmers to augment the concentration and composition 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 environmental conditions makes nutrient content analysis expensive and time-consuming. This paper 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 coexistence 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 | 2025-11-15T14:09:07Z |
| format | Article |
| id | upm-111545 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:09:07Z |
| publishDate | 2024 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1115452024-10-17T00:58:31Z http://psasir.upm.edu.my/id/eprint/111545/ A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms Ghazvini, Anahita Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lee, Lai Soon 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 regression algorithms to create a digital twin for farmers to augment the concentration and composition 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 environmental conditions makes nutrient content analysis expensive and time-consuming. This paper 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 coexistence 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 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf Ghazvini, Anahita and Mohd Sharef, Nurfadhlina and Balasundram, Siva Kumar and Lee, Lai Soon (2024) A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms. Applied Sciences, 14 (8). art. no. 3383. pp. 1-24. ISSN 2076-3417 https://www.mdpi.com/2076-3417/14/8/3383 10.3390/app14083383 |
| spellingShingle | Ghazvini, Anahita Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lee, Lai Soon 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/111545/ http://psasir.upm.edu.my/id/eprint/111545/ http://psasir.upm.edu.my/id/eprint/111545/ http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf |