Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models release_z6klkm3c4jhjlo63fcfx6lamkm

by CHIARA AMITRANO, Giovanni Battista Chirico, Stefania De Pascale, Youssef Rouphael, Veronica De Micco

Published in Sensors by MDPI AG.

2020   Volume 20, Issue 11, p3110

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.
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Type  article-journal
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Date   2020-05-31
Language   en ?
DOI  10.3390/s20113110
PubMed  32486394
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