Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models
Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to m...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3110 |
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| author | Chiara Amitrano Giovanni Battista Chirico Stefania De Pascale Youssef Rouphael Veronica De Micco |
| author_facet | Chiara Amitrano Giovanni Battista Chirico Stefania De Pascale Youssef Rouphael Veronica De Micco |
| author_sort | Chiara Amitrano |
| collection | DOAJ |
| description | Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (<i>Lactuca sativa</i> L. var. <i>capitata</i>). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm<sup>−2</sup>), edible biomass (RMSE = 6.87 g m<sup>−2</sup>), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios. |
| first_indexed | 2024-03-10T19:28:26Z |
| format | Article |
| id | doaj.art-247ccbf3d18b4ab293438fa2f50d2173 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:28:26Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-247ccbf3d18b4ab293438fa2f50d21732023-11-20T02:21:47ZengMDPI AGSensors1424-82202020-05-012011311010.3390/s20113110Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical ModelsChiara Amitrano0Giovanni Battista Chirico1Stefania De Pascale2Youssef Rouphael3Veronica De Micco4Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, 80055 Portici, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, 80055 Portici, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, 80055 Portici, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, 80055 Portici, ItalyProximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (<i>Lactuca sativa</i> L. var. <i>capitata</i>). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm<sup>−2</sup>), edible biomass (RMSE = 6.87 g m<sup>−2</sup>), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.https://www.mdpi.com/1424-8220/20/11/3110crop modellingenergy cascade model (MEC)<i>Lactuca sativa</i> L. var. <i>capitata</i>controlled environment agriculture (CEA)precision horticulture |
| spellingShingle | Chiara Amitrano Giovanni Battista Chirico Stefania De Pascale Youssef Rouphael Veronica De Micco Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models Sensors crop modelling energy cascade model (MEC) <i>Lactuca sativa</i> L. var. <i>capitata</i> controlled environment agriculture (CEA) precision horticulture |
| title | Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models |
| title_full | Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models |
| title_fullStr | Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models |
| title_full_unstemmed | Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models |
| title_short | Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models |
| title_sort | crop management in controlled environment agriculture cea systems using predictive mathematical models |
| topic | crop modelling energy cascade model (MEC) <i>Lactuca sativa</i> L. var. <i>capitata</i> controlled environment agriculture (CEA) precision horticulture |
| url | https://www.mdpi.com/1424-8220/20/11/3110 |
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