Using explanatory crop models to help decision support system in controlled environment agriculture (CEA)

Abstract

Lettuce (Lactuca sativa L.) is a popular leafy vegetable, widely grown and consumed throughout the world. Growing Lettuce plants in controlled environment, it is useful to increase the yield and obtain production year-round. In CEA (Controlled Environment Agriculture), computer technology is an integral part in the production and different sensors used to monitor environmental parameters and activate environmental control, are necessary. With the advent of technology, proximal sensors and plant phenotyping (in terms of physiological measurements of plant status) can help farmers in crop management. However, these kinds of tools are often expensive or inaccessible for stakeholders. The application of these tools to small-scale cultivation trials, could provide data for the implementation of mathematical models capable of predicting changes possibly happening during the cultivation. These models could then be applied at larger scales, as extensive farm production and be used to help in the cultivation management. In this study, green and red cultivars of Lactuca sativa L. ‘Salanova’ were grown in a growth chamber under controlled environmental condition (T, RH, light intensity and quality) in two trials under different vapour pressure deficit (VPD) : 1) VPD of 0.70 kPa (Low VPD; nominal condition) and 2) VPD of 1.76 (High VPD; off nominal condition). Plants were irrigated to field-capacity and weighted every-day in order to record daily ET; infra-red measurements were carried out to record leaf temperature and pictures were taken to monitor growth during the cultivation. Furthermore, after 23 days, on fully developed leaves, eco-physiological analyses (gas exchange and chlorophyll “a” measurements) were performed to assess the plant physiological behaviour in response to the different environmental conditions. Environmental data, were used as inputs in an energy cascade model (MEC) to predict changes in the plant daily growth, photosynthesis and evapotranspiration. The original model, was implemented with a few variations: leaf temperature (T) was used in place of air T for computing the stomatal conductance (gs) and the model parameters maxCUE and maxQY, were differentiated for the nominal and off-nominal scenarios and for green and red lettuce cultivars. After the validation against experimental data, this model appears to be a promising tool that can be implemented for forecasting variations triggered by anomalies in the environmental control. However, a next step will be to add a few parameters that will consider the intrinsic morpho-physiological variability of plants during leaf development.

Plant phenotyping relevance

植物の成長・光合成・蒸発散を予測するモデルを実装し、実験データで検証しており、植物状態の推定手法が研究の中心である。

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