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Currently the Dutch greenhouse horticultural sector has a high energy demand. The present use of weather forecasts can exacerbate this high energy consumption by contributing to suboptimal prediction and trading of the greenhouse's power demand. This study investigates the role of weather forecast errors on energy prediction power and trading uncertainty in greenhouse horticulture. This was done using an uncertainty analysis and computer model of a tomato producing Venlo style greenhouse in Bleiswijk, The Netherlands. This greenhouse model was used to predict the greenhouse's gas and electrical power demand. The study concluded that errors in the weather forecast of outdoor radiation, temperature and wind speed caused an overestimation of greenhouse energy demand. A sensitivity analysis showed that the radiation forecast error had the greatest impact on predicted greenhouse power demand errors with a mean relative error of 6.1 %. Predicted gas demand errors were most dependent on the outside wind speed forecast mean relative error (18.0 %) and temperature forecast error (17.2 %). A power trading uncertainty analysis was done to investigate the impact of predicted energy demand errors on the cost of buying power on the Dutch imbalance and Amsterdam Power Exchange day-ahead market. This cost analysis found that the volume of initial power trading was greater than corrective trading. Additionally, the higher volatility in short term power prices resulted in higher corrective power costs per unit of power than if the power demand had been initially predicted with more accuracy. • The study shows that weather forecasts create energy demand uncertainty. • A data-based sensitivity analysis was done for input data weather variables. • The greenhouse power costs are calculated using imbalance and day-ahead markets. • As the weather forecasts lengthen, the energy prediction error and power costs increase. • The radiation forecast has the greatest impact on the predicted power error. [ABSTRACT FROM AUTHOR]

The increasing demand for efficiently growing vegetation in greenhouses requires continual improvement of the control of the growth environment experienced by the plants. The single most important factor in maximizing the crop's growth is the quantity, quality, and geometrical distribution of radiation intercepted at every moment. Few available greenhouse coverings are capable of responding to changes in sunlight conditions by themselves, limiting the grower's control: they must use additional technologies, including screens, artificial lighting, heating, and cooling. This review considers existing efforts in providing adaptable greenhouse covering systems and advanced optical materials for controlling the color, intensity, and/or distribution of sunlight transmitted into greenhouse‐like structures by describing existing static materials and their responsive equivalents. This work also offers speculation on potential applications of other light‐control elements, mostly designed for use in the urban environment that can be adapted for greenhouse use in the future. [ABSTRACT FROM AUTHOR]

Greenhouses shelter the crop from unfavourable environmental conditions and the covering largely contributes to creating beneficial growing conditions inside. There is no perfect greenhouse cover for all combinations of crop and climatic regions. Usually a greenhouse cover has permanent optical properties determining the amount of solar radiation entering the greenhouse. Consequently during crop growth, the amount and quality (spectrum, direct/diffuse ratio) of the solar radiation is not ideal for the crop. Growers try to compensate for this by using different additional techniques such as temporary coatings, screens (mobile or fixed, etc.) and heating or cooling. New materials are currently being developed, whose optical properties can be (almost) instantaneously changed (materials with switchable properties). This will allow growers to gain real-time control on the quantity and quality of the light entering the greenhouse to match crop requirement. The present study uses advanced simulation models to predict the potential of covers with switchable properties to improve tomato yield and use of resources in different climatic regions (mild winter and tropical) and with different greenhouse types (artisan and industrial type). Results indicate that covers with switchable properties have advantages over permanent properties for most combinations of filter type/location. Only in very extreme tropical climates will covering materials with permanent filter properties have advantages. Furthermore, simulations models can play a major role in optimising the switchable filter design. • No single type of greenhouse cover has optimum performance in every climate & location. • Switching radiometric properties looks promising for high light demand crops. • Switchable materials have no advantage over permanent filters in tropical climates. • Simulation could be a powerful tool to determine optimum radiometric properties. • Simulation should include specific combination of crop, greenhouse location & type. [ABSTRACT FROM AUTHOR]

The rapid urban growth seen globally in recent years has not been supported by a simultaneous increase in agricultural land and/or crop productivity. Producing crops in (peri‐)urban areas shows good potential to provide the vegetable products for a healthy and balanced diet for the growing population, but it has to deal with the local availability of resources. Thus, meeting the food requirements of the urban population as efficiently and robustly as possible is a challenge. This study developed a methodology to estimate the use of resources of urban farming systems to produce energy‐ and nutrient‐dense vegetables capable of meeting human dietary needs. The method was applied to two extremely different cultivation systems (an open field farm and a plant factory with artificial lighting) for the production of seven crops. The results on the resource efficiencies to meet the annual per‐capita vegetable requirements are discussed in relation to crop type, local climate and cultivation system. The application of this methodology can support farmers' decisions on the choice of crops and the type of urban farming systems that are most efficient in contributing to a plant‐based diet. The results can also be translated into water, energy, and surface area needed to meet the nutritional requirements at a city‐regional level. [ABSTRACT FROM AUTHOR]

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second "Autonomous Greenhouse Challenge". In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production. [ABSTRACT FROM AUTHOR]

Recent studies indicate that food demand will increase by 35–56% over the period 2010–2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition "Autonomous Greenhouse Challenge". This paper describes and analyzes the results of the third edition of this competition. The competition's goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO2 were decisive in realizing the competition's goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work. [ABSTRACT FROM AUTHOR]

LED lighting is appointed as the successor of HPS lighting in greenhouses since it can lead to a more sustainable cultivation, i.e. it converts electrical energy into photosynthetically active radiation more efficiently. To quantify the effect of this more efficient conversion within the operation of the greenhouse system, an optimal controller is proposed to generate optimal control trajectories for the controllable inputs of the greenhouse. The optimal controller makes use of an economic objective function, i.e. the difference between income (y i e l d × p r o d u c t p r i c e) and cost of resources (r e s o u r c e u s e × c o s t). The performance of this optimally controlled greenhouse system is compared with respect to the state-of-the-practice. Simulation experiments suggest optimal control can increase the economic objective by 10 % to 65.14 €. m − 2 compared to 58.96 €. m − 2 for the state-of-the-practice, for tomatoes cultivated in a Dutch weather conditions. The model of the optimally controlled greenhouse is used to compare the performance of different lighting systems, i.e. no lighting, HPS lighting and LED lighting. An increase of 9 % in the operational return is observed for LED lighting compared to HPS lighting. The electricity that is saved due to the more energy-efficient conversion in the LED lighting results in a 30 % decrease in carbon footprint when comparing a greenhouse with LED lighting to a greenhouse with HPS lighting. • A model suitable for the control of the greenhouse climate is developed. • The model allows for the optimization of a purely economic cost function. • Optimal control increases the return by 10% compared to the state-of-practice. • Simulations suggest a 9 % increase in return when using LED lights instead of HPS. • Carbon footprint decreases by 30 % when using LED lights instead of HPS. [ABSTRACT FROM AUTHOR]

Mobile rose systems are designed to increase labour efficiency. However, many questions remain on best design and settings of operational parameters for best performance. The ultimate goal of this research is an assessment of re-designed horticultural crop production systems and work scenarios on labour and machine performance before implementation. To attain this goal, a queueing network model, GWorkS 1 [1] GWorkS is an acronym for Greenhouse Work Simulation, and is a discrete event simulation model on the crop handling processes and logistics inside a greenhouse production site. -rose, is presented for simulation of labour processes in a greenhouse with a mobile rose cultivation system. The objective for modelling is to quantify effects of production system changes by means of a flexible and generic model approach. A state-of-the-art mobile rose production system was used to validate and test the GWorkS-rose model. Data from the labour registration system and from video recordings were used for validation. System performance was simulated and compared to the performance measured in the real situation. Results of a single day validation show that the model estimates harvest labour time with an accuracy of 94%. For a one month validation an accuracy of 92% and RRMSE of 18% resulted. The value of RRMSE was caused by missing data on the number of workers at the loop and on the actual gutter speed level. The model can determine best system settings as is illustrated for operator and mobile gutter speed settings at given rose yield levels. It is concluded that the model can be used for studies on design and management of this kind of production systems. [Copyright &y& Elsevier]