Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems
Abstract As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional fa...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02763-9 |
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| author | Benedetta Fasciolo Nicolò Grasso Giulia Bruno Paolo Chiabert |
| author_facet | Benedetta Fasciolo Nicolò Grasso Giulia Bruno Paolo Chiabert |
| author_sort | Benedetta Fasciolo |
| collection | DOAJ |
| description | Abstract As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional farming methods struggle with efficiently managing nutrients and water while ensuring high-quality products, leading to resource wastage and food safety concerns. This study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems, thereby enhancing resource management and product quality. We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. The model was validated with real-time data from aeroponic systems, showing good predictive performance, particularly for fresh weight and total leaf area. In contrast, predictions of nitrate content and water consumption were less accurate, due in part to smaller training datasets and limitations of the physics-based component under soilless conditions. Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability. |
| format | Article |
| id | doaj-art-c8022c01d0ae4692b29aa55199aac69a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c8022c01d0ae4692b29aa55199aac69a2025-08-20T03:45:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-02763-9Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systemsBenedetta Fasciolo0Nicolò Grasso1Giulia Bruno2Paolo Chiabert3Politecnico di TorinoPolitecnico di TorinoPolitecnico di TorinoPolitecnico di TorinoAbstract As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional farming methods struggle with efficiently managing nutrients and water while ensuring high-quality products, leading to resource wastage and food safety concerns. This study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems, thereby enhancing resource management and product quality. We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. The model was validated with real-time data from aeroponic systems, showing good predictive performance, particularly for fresh weight and total leaf area. In contrast, predictions of nitrate content and water consumption were less accurate, due in part to smaller training datasets and limitations of the physics-based component under soilless conditions. Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability.https://doi.org/10.1038/s41598-025-02763-9Hybrid modelMachine learningPhysics-based modelAeroponicIoTPredictive model |
| spellingShingle | Benedetta Fasciolo Nicolò Grasso Giulia Bruno Paolo Chiabert Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems Scientific Reports Hybrid model Machine learning Physics-based model Aeroponic IoT Predictive model |
| title | Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems |
| title_full | Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems |
| title_fullStr | Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems |
| title_full_unstemmed | Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems |
| title_short | Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems |
| title_sort | hybrid machine learning and physics based model for estimating lettuce lactuca sativa growth and resource consumption in aeroponic systems |
| topic | Hybrid model Machine learning Physics-based model Aeroponic IoT Predictive model |
| url | https://doi.org/10.1038/s41598-025-02763-9 |
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