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.

Light and Low Relative Humidity Increase Antioxidants Content in Mung Bean (Vigna radiata L.) Sprouts

“…Agricultural practices are changing with the introduction of new technologies for the remote monitoring and controlling of environmental conditions, such as intelligent sensors, robots, proximal/remote sensing [ 1 , 2 ]. These new technologies applied to agriculture are helping to meet the nutritional requirements of an increasing population, minimizing environmental impacts and coping with the scarcity of resource availability, also exacerbated by climate changes [ 3 ].…”

Metrology for Agriculture and Forestry 2019

“…Advanced sensing technologies are also used in controlled environments in combination with crop growth models. Paper [ 18 ] applied this technology for predicting crop responses to microclimatic changes, as part of decision support systems designed for optimal crop system management in controlled environments.…”

Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling

“…They are also characterised by having a complex substrate partitioning strategy with different parts being coordinated to uptake and distribute specific compounds. This has contributed to the preference of empirical at the expense of mechanistic models due to the usually satisfactory information provided by the former, especially in nominal conditions ( Boscheri et al., 2012 ; Amitrano et al., 2020 ). Empirical models cover a limited range of operating conditions though; thus, the scope of their use is narrow and cannot contemplate all scenarios that plant culture may undergo.…”