No abstract

Controlling plant height without the use of plant growth retardants is one of the goals in future production of potted plants. Light quality with a low red to far-red ratio (R:FR) increases plant height. In this trial, the effects of light quality [R:FR ratio of 0.4, 0.7, and 2.4 (R = 600–700 nm, FR = 700–800 nm)] at the end of day were investigated on potted chrysanthemums using growth chambers. After a 9-h photoperiod, the 30-min end-of-day lighting was provided by light-emitting diodes at low irradiance by maintaining either red = 1 μmol·m−2·s−1 (Rcon) or far-red = 1 μmol·m−2·s−1(FRcon). After 3 weeks of end-of-day lighting, plants given the lowest end-of-day ratios (R:FR of 0.4 or 0.7) were taller than control plants (R:FR = 2.4). For low ratios of R:FR (0.4), the actual intensities of R and FR did not affect plant height, whereas for higher ratios of R:FR (0.7 and 2.4), plant height was greater for FRcon than for Rcon. Leaf area of the lateral side shoots was lower for plants treated with an R:FR of 0.4 compared with those of controls. Dry weight, stem diameter, number of internodes, and number of lateral branches were unaffected by the end-of-day ratio.

There is an increasing demand for industry specific solutions for optimizing production processes with the transitions towards Industry 4.0. The commercial greenhouse sector relies heavily on optimal use of energy with multiple new concepts introduced in recent years e.g. vertical farming and urban agriculture. Digital twins allow utilizing Internet of Things and big data to simulate the alternative operation strategies without compromising current operation. This paper aims to present the development of a digital twin of the commercial greenhouse production process as a part of the recently launched EUDP funded project Greenhouse Industry 4.0 in Denmark. This digital twin allows using big data and the Internet of Things to optimise the greenhouse production process and communicate with other digital twins representing essential areas in the greenhouse (climate and energy). This digital twin can estimate future states of the greenhouse by using past and real-time data inputs from databases, sensors, and spot markets. This paper also introduces a Smart Industry Architecture Model Framework for the discussion of the required data architecture of the digital twin for the greenhouse production flow which ensures a correct data architecture for the data exchange across all entities in the system.

Production in a dynamic photosynthesis optimized climate (DC) was compared to production in a traditional and more stable climate (TC). Production of a tropical plant species (Hibiscus rosa-sinensis L.) in a DC resulted in between 18% and 63% reduction in energy use, mainly due to lower temperatures and increased use of thermal screens. In high light periods, the average day temperatures (ADT) were virtually the same in the different treatments, while in low light periods both ADT and average night temperature (ANT) were lower in the DC. Differential use of the screens resulted in a higher cumulative light integral in the DC. The number of lateral breaks was either the same or higher in the DC. Dry weight at the end of the production period was not significantly different in six of the seven experiments, and in five out of seven replications, plants grown in the DC were shorter than plants in the TC. Production periods between 10 days shorter and 21 days longer, for the DC compared to the TC, could not be explained by temperature integration alone. In the DC, a high positive DIF (difference between ADT and ANT) does not seem to increase elongation growth. The study illustrates that it is possible to produce a heat-demanding plant and save energy using a DC.

Greenhouse energy-saving and biocide reduction can be achieved through dynamic greenhouse climate control with computerized model-based regimes. This can be optimized when next to greenhouse macroclimate (i.e., the aerial environment) also, the crop microclimate is predicted. The aim of this article was to design and apply a simple deterministic microclimate model for dynamic greenhouse climate control concepts. The model calculates crop temperature and latent heat of evaporation in different vertical levels of a dense canopy of potted plants. The model was validated with data attained from experiments on dynamic or nondynamic (regular) controlled greenhouse cultivation. Crop temperature was with a 95% confidence interval of 2 °C or 2.4 °C for sunlit or shaded leaves, respectively, accurately predicted in a simple greenhouse with predefined climate set points. With a more dynamic greenhouse control also including assimilation lighting and screens, the prediction quality decreased but still had a 95% confidence interval of crop temperature prediction of 3.8 °C for sunlit leaves. Simulations showed that controlling greenhouse temperature according to the predicted crop temperature rather than according to the air temperature can save energy. Energy-saving is highest during winter and 12% energy saving was attained during January under Danish climate conditions.