Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Abstract

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 ( Lactuca sativa L. var. capitata ). 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 -2 ), edible biomass (RMSE = 6.87 g m -2 ), 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.

Plant phenotyping relevance

植物の生理・成長形質を予測する数学モデルを改良し、独立データセットで検証しており、形質推定手法の検証が中心です。

Code and data availability

The article describes a modified energy cascade (MEC) model calibrated and validated on lettuce experiments, but no public phenotype dataset, image/sensor data deposit, author analysis code, or trained model checkpoint is mentioned. The only external URL in the text is a cited CGIAR report (reference 6), which is prior

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