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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]

Background: Controlled environment agriculture, particularly vertical farms (VF), also called plant factories, is often claimed as a solution for global food security due to its ability to produce crops unaffected by weather or pests. In principle, essential macronutrients of the human diet, like protein, could technically be produced in VF. This aspect becomes relevant in the era of protein transition, marked by an increasing consumer interest in plant‐based protein and environmental challenges faced by conventional farming. However, the real question is: what does the cultivation of protein crops in VF imply in terms of resource use? To address this, a study was conducted using a VF experiment focusing on two soybean cultivars. Results: With a variable plant density to optimize area use, and because of the ability to have more crop cycles per year, protein yield per square metre of crop was about eight times higher than in the open field. Assuming soy as the only protein source in the diet, the resources needed to get total yearly protein requirement of a reference adult would be 20 m2 of crop area, 2.4 m3 of water and 16 MWh of electricity, versus 164 m2, 111 m3 and 0.009 MWh in the field. Conclusions: The study's results inform the debate on protein production and the efficiency of VF compared to conventional methods. With current electricity prices, it is unlikely to justify production of simple protein crops in VF or promote it as a solution to meet global protein needs. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This paper presents a receding horizon optimal control (RHOC) method with an economic objective function for balancing the cost of resources (resource use × cost) with income through yield (yield × product price). This paper considers the two elements that determine the income through yield. The first element is yield and associated fruit development. A new, computationally viable, approach to model the income through yield is proposed and its prediction accuracy with respect to the original model is evaluated. The new approach employs a model that predicts at each time step, the future income through yield based on the assimilates partitioned to the fruits at the current time step. Simulations suggest that the assumptions made to arrive at the model for the new approach, do not significantly affect the accuracy of the predictions. The second element considered in this paper is the product price and the uncertainty inherent in its forecasts. Historical product price data are used to generate artificial product price forecasts. An uncertainty analysis, in combination with the artificial product price forecasts, showed that the product price forecast error does not considerably affect the optimised control strategy. Season-wide simulations with RHOC suggest that the product price forecast error does not considerably affect the value of the economic objective function. • A computationally viable approach to model the income through yield is proposed. • The new approach to model income through yield allows for RHOC with a short horizon. • Historical product price data are used to create artificial product price forecasts. • Product price forecast errors do not considerably affect the control strategy. [ABSTRACT FROM AUTHOR]

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on "autonomous greenhouses" aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference. [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]

Crop modelling is an essential part of biosystems engineering; selecting or developing a crop model for a specific application, having its requirements and desires, is difficult if not impossible without the required domain knowledge. This paper presents a fundamentally different model selection approach based on biological functionalities. This is enabled by a common structure, which allows for a combining of components, yielding new models. This increased design space allows the development of models which are better suited to the application than the original models. The use of a common structure, and its potential, are demonstrated by a use-case involving the selection of a tomato crop model for a model-based control application, but the rationales and methodologies can apply to other crops and applications as well. In this paper, 27 valid model combinations have been created from 4 models. In the use-case presented, the models are compared to data originating from a real system. The predictive performance of a model is quantified by the Root-Mean-Squared-Error (RSME) between the predictions and data. One trade-off is model accuracy versus computational speed. With the model set used, a 13% decrease in RSME was obtained by allowing a 7.5% increase in model computation time compared to one of the original models. • A fundamentally different model selection approach using biological functionalities. • 27 valid model combinations have been created from 4 models. • A model combination has been found which predicts better than the original models. [ABSTRACT FROM AUTHOR]

Worldwide competitive challenges urge growers to further improve operational performance. In this paper, the objective ‘model-based analysis and improvement of the operation of horticultural production systems’ was narrowed to ranking simulated labour management scenarios in a multi-operations and multi-worker static cut-rose cultivation system. Eight scenarios with worker skill as a central theme were simulated including a practical labour management scenario applied by a Dutch cut-rose grower. The GWorkS-model was prepared for simulation of disbudding and bending in addition to harvest, three crop operations representing over 90% of crop-bound labour time, as well as for full scale simulation of the greenhouse using all workers and equipment. The sub-models on disbudding and bending were verified using data acquired in practice. Both processes were reproduced accurately. The model study on work scenarios showed that labour organisation choices might yield up to 5 s per harvested rose difference in total labour time for harvest, bending and disbudding between the best and worst scenario, which is equivalent to 7.1 € m −2 labour costs difference per year. Scenarios pointed out that working with low skilled, low paid workers is not effective. Specialised workers were most time effective, −17.5% compared to the reference, but overall a permanent team of skilled generalists ranked best in a multi-factorial assessment. Reduced crop operation diversity per day improved labour organisational outputs but ranked almost the same as the reference. The reference scenario was outranked by 5 scenarios. Overall, the GWorkS-model provided clear answers to research questions using the full complexity of crop operations. [ABSTRACT FROM AUTHOR]

Combination of production of algae and tomato increases efficient use of available resources of greenhouse enterprises, such as controlled environment, water and nutrients, carbon dioxide, greenhouse space and infrastructure and knowledge. No information is available, however, about the potential productivity and related costs of a combined tomato and algae production in Dutch greenhouses. The objective was to determine the algae productivity in tubular photobioreactors (PBRs) and the economic feasibility of combined production of tomato and algae in Dutch greenhouses. A model was developed to predict greenhouse climate from outside climate, to predict tomato and algae biomass production and to analyse scenarios of different locations and dimensions of tubular PBR in the greenhouse with regard to algae productivity and cost price of algae production. The results show that algal productivity is low if PBRs are installed under a tomato crop due to limited light levels. Areal algal productivity was calculated to be 5–6.5 kg DM m−2 if PBRs are installed in a separate greenhouse compartment next to tomato. In this case the minimum cost prices of algae production was calculated to be €11 kg−1 DM algae, which give perspectives for the future. The proposed model is important because it gives insight into the feasibility of algae and tomato production in Dutch greenhouses. This novel model approach and the scenario results provide better knowledge about the potential productivity and related costs and returns of algae production in greenhouses. [ABSTRACT FROM AUTHOR]

Labour is the most dominant cost factor in Dutch cut-rose production. To improve crop production systems and labour management, a generic process modelling approach was developed enabling the impact of different scenarios on labour productivity to be assessed. The crop production system with crop handling processes is defined as a stochastic discrete event system. This paper demonstrates the model flexibility and transferability by adapting an existing model developed for a mobile rose production system to a model for a static growing system for cut roses. The paper describes the adaptation process. The adapted model was validated for the harvest process at a 3.6 ha production site in the Netherlands. Work scenarios were simulated to examine effects of skill, equipment, and harvest management. The model reproduces the harvest process accurately. A seven workday validation for an average skilled harvester showed a relative root mean squared error (RRMSE) under 5% for both labour time and harvest rate. A validation over 96 days for various harvesters showed a higher RRMSE, 15.2% and 13.6% for labour time and harvest rate respectively, mainly caused by the absence of model parameters for individual harvesters. The model was successfully used in scenario studies and indicated that worker skill was an important cost factor, differences associated with harvest trolley type are small, and that an extra harvest cycle per day is only feasible when compensated by product price. Overall, the generic model concept performs well for a static growing system when extended with system specific properties and process elements. [Copyright &y& Elsevier]

Greenhouse crop system design for maximum efficiency and quality of labour is an optimisation problem that benefits from model-based design evaluation. This study focussed on the harvest process of roses in a static system as a step in this direction. The objective was to identify parameters with strong influence on labour performance as well as the effect of uncertainty in input parameters on key performance indicators. Differential sensitivity was analysed and results were tested for model linearity and superposability and verified using the robust Monte Carlo analysis method since in the literature, performance and applicability of differential sensitivity analysis has been questioned for models with internal stochastic behaviour. Greenhouse section length and width, single rose cut time, and yield influence labour performance most, but greenhouse section dimensions and yield also affect the number of harvested stems directly. Throughput, i.e. harvested stems per second, being the preferred metric for labour performance, is most affected by single rose cut time, yield, number of harvest cycles per day, greenhouse length and operator transport velocity. The model is insensitive for σ of lognormal distributed stochastic variables describing the duration of low frequent operations in the harvest process, like loading and unloading rose nets. In uncertainty analysis, the coefficient of variation for the most important outputs, labour time and throughput, is around 5%. Total sensitivity as determined using differential sensitivity analysis and Monte Carlo analysis essentially agreed. The combination of both methods gives full insight into both individual and total sensitivity of key performance indicators. [Copyright &y& Elsevier]

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]

An optimisation algorithm, as an essential part of a model-based method to design greenhouses for a broad range of climatic and economic conditions, was described. This algorithm – a modified controlled random search using parallel computing – maximised the annual Net Financial Return (NFR) for a tomato grower by selecting the best alternative to fulfil eight design elements: type of greenhouse structure, material of the cover, outdoor shade screen, whitewash properties, thermal screen, heating system, cooling system and CO2 enrichment system. As an example, the algorithm was applied to two locations with different climatic and economic conditions, Almeria and The Netherlands. Due to the warm climate with high radiation levels in Almeria, a greenhouse with a relatively large specific ventilation area (20% compared to 14% for Dutch conditions), seasonal whitewash and a low-capacity direct air heater (50 W m−2 compared to 200 W m−2 for Dutch conditions) was selected. In contrast, for the relatively cold climate with low radiation levels of the Netherlands, a 100% aluminium thermal screen and no whitewash would give the best result. The design method produced realistic greenhouses and related annual NFR, indicating that the method performs well. An analysis of the close-to-best greenhouses showed that, for both locations, a structure with high light transmissivity considerably enhanced the greenhouse performance whereas an outdoor shade screen, geothermal heating and mechanical cooling would be not economical. These results demonstrate the feasibility of a model-based design approach that produces suitable greenhouse designs for given climatic and economic conditions. [ABSTRACT FROM AUTHOR]

The aim of study was to evaluate if the alternation in growth stage–specific lighting spectrum would be superior for tomato growth, photosynthesis, and mineral element contents compared to constant spectrum lighting. Dwarf tomato (Solanum lycopersicum L. cv. Micro Tom) was cultivated in controlled environment chamber (23/19 °C) under light emitting diode lighting. Three lighting spectrum treatments were set, optimized for different tomato growth stages: "seedling" (S; blue (B, 447 nm), red (R, 660 nm) and far red (FR, 740 nm) light), "growth" (G; R, B and FR light, supplemented with 523 nm green) and fruiting (F; R, B, FR light supplemented with 385 nm ultraviolet A (UV-A)). The total photon flux density of 250 μmol m−2·s−1 was maintained in all treatments. Three lighting spectrums were alternated in seedling (S, G, F), biomass growth (SS, SG, GG, FF) and fruiting (SSS, SGG, GGG, GGF, FFF, SGF) stages of tomato creating growth stage-specific or constant lighting spectrum strategies. The light effects depended on tomato age, however the alternation in growth stage-specific lighting spectrum did not have a pronounced impact on dwarf tomato photosynthetic indices, growth, yield and mineral element content. The investigated parameters mainly depended on the spectrum of the latter growth stage. [ABSTRACT FROM AUTHOR]