Greenhouse Climate Control Research Articles

Microclimate monitoring in commercial tomato (Solanum Lycopersicum L.) greenhouse production and its effect on plant growth, yield and fruit quality

IntroductionHigh annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself.MethodsIn this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients.Results and discussionDifferent microclimates were present throughout the year, with seasonal (spring – summer – autumn) and diurnal (day – night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield – maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions.

Benchmarking techno-economic performance of greenhouses with different technology levels in a hot humid climate

Greenhouse agriculture is expected to play a critical role in sustainable crop production in the coming decades, opening new markets in climate zones that have been traditionally unproductive for agriculture. Extreme hot and humid conditions, prevalent in rapidly growing economies including the Arabian Peninsula, present unique design and operational challenges to effective greenhouse climate control. These challenges are often poorly understood by local operators and inadequately researched in the literature. This study addresses this knowledge gap by presenting, for the first time, a comprehensive set of benchmarks for water and energy usage, CO2 emissions (CO2e) contribution, and economic performance for low-, mid-, and high-tech greenhouse designs in such climates. Utilising a practical and adaptable model-based framework, the analysis reveals the high-tech design generated the best results for economic return, achieving a 4.9-year payback period with superior water efficiency compared to 5.8 years for low-tech and 7.0 years for mid-tech; however, the high-tech design used significantly more energy to operate its mechanical cooling system, corresponding with higher CO2e per unit area (8.3 and 4.0 times higher than the low- and mid-tech, respectively). These benchmarks provide new insights for greenhouse operators, researchers, and other stakeholders, facilitating the development of effective greenhouse design and operational strategies tailored to meet the challenges of hot and humid climates.

Automated Hydroponic Farm System

Current agriculture faces a multitude of issues – limited seasons, excessive pesticide use, sustainability concerns in organic farming, high carbon footprint, and inconsistent produce quality. Traditional methods, restricted by seasons and harming the environment with pesticides, are struggling to keep up with a changing world. This paper proposes a groundbreaking solution: an automated hydroponic farm system. Unlike traditional methods, this system allows year-round, pesticide-free production through advanced growing stations using a precise nutrient delivery technique (NFT). To create perfect growing conditions, a Greenhouse Climate Control System with various sensors regulates factors like temperature, humidity, and light. Additionally, a Central Console acts as the brain of the system, connecting everything and monitoring the entire farm. By automating these processes, this hydroponic system aims to revolutionize agriculture, offering a sustainable, efficient, and high-quality alternative.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

Decarbonization through smart energy management: Climate control in building-integrated rooftop greenhouses for urban agriculture across various climate conditions

This paper investigates the potential benefits of integrating rooftop greenhouses (RTGs) with buildings to reduce energy and CO2 costs, contributing to urban decarbonization efforts. This integration not only advances sustainable urban agriculture but also aligns with the United Nations Sustainable Development Goal 7 (SDG7) - Affordable and Clean Energy, and Sustainable Development Goal 11 (SDG11) - Sustainable Cities and Communities, through its energy-efficient, low-carbon approach. We hypothesize that this integration can significantly lower energy use in integrated RTGs (i-RTGs) and set out to validate this through dynamic models for both building and i-RTG climates, focusing on CO2 concentration, humidity, and temperature. Central to our approach is the implementation of a nonlinear model predictive control (NMPC) framework, which utilizes these dynamic models to make control decisions aimed at minimizing total control costs. This framework operates in a receding horizon procedure, regulating climate factors within the i-RTG and building using various actuators. We validate our approach by simulating an i-RTG atop a building in eleven different cities, demonstrating interactions such as energy, moisture, and CO2 exchange. Significantly, our study reveals that integrating i-RTG with buildings under the NMPC framework leads to a 15.2% reduction in control costs. Additionally, we find that the i-RTG model is adaptable to different climates, with colder regions showing greater cost reduction potential. Our study underscores the viability and advantages of integrating i-RTGs with buildings, offering a sustainable, decarbonizing solution for urban agriculture and building management that contributes to the fulfillment of SDG7 and SDG11.

Working like Machines: Technological Upgrading and Labour in the Dutch Agri-food Chain

This article engages with the role of technological upgrading for work in agriculture, a sector commonly disregarded in debates about the future of work. Foregrounding migrant work in Dutch horticulture, it explores how technological innovation is connected to the scope and security of employment. Besides, it proposes a heuristic that connects workers’ experience to sectoral dynamics and the wider agri-food chain. Our analysis reads data from a small-scale qualitative study with different actors in the Dutch agri-food sector through the lens of the global value chain literature. Nuancing pessimistic predictions of widespread technological unemployment, we find product upgrading into high value-added products, and process upgrading, such as through climate control in greenhouses, to offer the potential for more and secure employment. However, higher work intensity and the dismantling of entitlements for rest and reproduction to ‘make people work like machines’ represent the underbelly of these dynamics.

Optimized model predictive control for solar assisted earth air heat exchanger system in greenhouse

Agricultural greenhouses play a crucial role in addressing the threat of climate change to agricultural systems. The greenhouse systems control exhibits nonlinear characteristics and large lag, both affecting the regulation of the greenhouse thermal environment. In this paper, a control strategy was designed to set the dynamic reference value of the control objective of the greenhouse model, considering factors such as solar radiation and the heating capacity of water body. A complete greenhouse model was developed using TRNSYS, and then the model predictive control building and optimization were implemented through MATLAB. The control effects of traditional proportional-integral-differentiation control, initial model predictive control, and optimized model predictive control were compared. The results showed that the regulation of greenhouse temperature was improved by 10.6 % in the coldest mouth compared to the conventional proportional-integral-differentiation control by the optimized model predictive control. During a typical cold month, the violation of constraints was reduced by 29.7 % by dynamically setting the target of the greenhouse with the optimized model predictive control. The performance of greenhouse climate control is enhanced by the proposed optimized model predictive control method, ensuring a stable regulation of the crop growth environment.

Building a reduced order model from CFD data on leaves to evaluate optimal climate conditions

Climate control in vertical farms or greenhouses is crucial in order to have the optimal climate for the plants. This climate can be simulated using Computational Fluid Dynamics (CFD). This way, different ventilation methods can for example be compared. Previously, plants were modeled as a porous zone, in which additional source terms are added to the energy or transpiration transport equation. In this study, ten different leaves under different orientations are studied. The heat and mass balance for each leaf is solved and the boundary layer is adequately resolved. Different leaf orientations and positions to the flow are used, so that each leaf has its own distinct boundary layer. Leaf temperature and humidity are a function of the incoming flow, but also of the incoming temperature, humidity and stomatal resistance. In order to be able to quickly assess the effects of changing boundary conditions, on the temperature and humidity of the leaves, a Reduced Order Model (ROM) is made from the CFD-data. This method allows to quickly assess the effects of a stomatal resistance, the incoming radiation or a changing inlet temperature or humidity. A ROM was built using the data from one simulation. In order to see whether the ROM is able to predict other configurations, two additional cases were solved in CFD and were compared to the ROM. Leaf temperatures and humidities only differed maximally 3.14% and were quite good at predicting this. Predicted leaf transpiration rates differed slightly more, as these differed maximally 10.5%. Overall, the ROM has proven to be a valuable and numerically cost-effective tool to predict overall leaf climate and can be a good tool when later the climate of a full plant is simulated.

Optimizing winter climate control in high-altitude smart greenhouse through renewable energy integration

In the context of food security in mountain regions, this study explores the potential of a Smart Greenhouse (SGH) to address the challenges posed by climate change and limited resources. The SGH utilizes an 8 m2 solar water heating system, weather stations for external temperature and humidity monitoring, as well as internal light, humidity, and temperature sensors to create an optimized environment for crop cultivation. Experiments were conducted with various crops within the SGH during the harsh winter months, typically rendering the cultivable land fallow due to extreme cold conditions. The SGH is equipped with sensors and communication technologies, ensuring continuous data collection and the automated regulation of heating, ventilation, and air conditioning systems. Located in Genekha, Thimphu, this pilot SGH seamlessly integrates renewable energy technology to provide heat during the cold winter season, obviating the need for external heating sources in the summer. The study employed TRNSYS and OpenStudio simulation software to assess the SGH's performance, focusing on temperature and humidity control inside and outside the greenhouse. Additionally, soil pH and nutrient content were examined, and the time required for crop growth and harvesting was recorded. In conclusion, this paper acknowledges some of the current design's limitations and presents recommendations for future enhancements, with a focus on the specific context of improving crop production and food security in mountainous regions.

Chance-constrained stochastic MPC of greenhouse production systems with parametric uncertainty

Greenhouse climate control is important to provide sufficient fresh food for the growing population in an economical and sustainable manner. However, the developed crop-climate models are generally complex with parametric uncertainties and far from describing the real system accurately, which affects adversely the control system’s performance. To improve optimality and guarantee robustness during the control process, we develop and implement a stochastic model predictive control (MPC) scheme for greenhouse production systems considering parametric uncertainties. By leveraging the advantages of model linearization, the proposed chance-constrained MPC method enables a more straightforward formulation of uncertainty constraints and computationally cheaper optimization in comparison to directly using the nonlinear model. Finally, the efficacy of the proposed approach is demonstrated on a greenhouse climate control case study.

Nonlinear PID-based temperature control techniques in greenhouses using natural ventilation

Greenhouses provide controlled environments for cultivating crops. This work explores the design of PID-based greenhouse climate control techniques using natural ventilation, considering both measurable disturbances and operational constraints. A nonlinear model was developed to relate natural ventilation (the primary climate actuator) with interior temperature in greenhouses, the critical factor being the relationship between air flow rate and vent aperture, which is a source of uncertainty in the related literature. Novel calibration issues are included for this term. The nonlinear model is used to devise control strategies combining PI control and feedback linearization. The resulting simulations and robustness analysis, which accounts for uncertainties in the relevant parameters, demonstrate that the combination of PID and feedback linearization techniques is a suitable control approach. In summary, practical tradeoffs can be achieved between setpoint tracking, disturbance rejection, control effort, and robustness.

Application of Fractional-order PID controllers in a Greenhouse Climate Control System

A model-based Fractional Order Proportional-Integral-Derivative (FOPID) control design method is applied to a multivariable greenhouse climate control system. A multivariable transfer function model of the greenhouse system, obtained via model Identification experiment, provides plant’s model information for control design. The fractional controller is tuned using the conventional Biggest Log-modulus Tuning (BLT) method associated with integer order PID controllers. However, unlike the conventional PID case, the controller gains are first computed using internal model control design procedure. This produces preliminary values of controller parameters. The FOPID controller gains are further tuned using a single detuning factor (F) until a biggest log modulus of 2N dB is obtained where N is the number of loops in the MIMO system. Simulation studies, using MATLAB and Simulink, show that good compromise between performance and robustness is achievable for both temperature and humidity regulation in a greenhouse control application.

Investigating an Enhanced Approach for Greenhouse Climate Control: Optimising Cooling and Heating Systems

This study aimed to enhance greenhouse climate regulation by optimizing the efficiency of existing cooling and heating systems while considering external temperature and humidity conditions. We introduced an automated system capable of regulating the internal greenhouse environment, which underwent testing across 30 days in September 2021, with temperatures ranging from 26°C to 31°C and humidity from 65% to 70%. The system consistently monitored and adjusted the microclimate, with sensors capturing temperature and humidity data at 30-second intervals, amassing over 83,000 data entries for enhanced control accuracy. The automated regulation effectively maintained desired humidity, significantly reducing nighttime levels by 80% while carefully increasing daytime humidity to counteract external heat. Temperature control was largely successful, sustaining daytime levels around 32°C, but faced challenges in maintaining the target of 26°C during cooler nights. Energy consumption was optimized, with the automation leading to a significant 4-33% energy saving for cooling and an 8% saving in heating compared to traditional methods. Additionally, the system was accessible via a web interface, allowing for real-time climate tracking and prompt anomaly identification. In conclusion, the developed greenhouse automation system exhibited efficiency in equipment usage and improved temperature and humidity control. Further enhancements are required for lamp-based heating. This research contributes to the efficiency and reliability of greenhouse automation systems, mitigating risks associated with external environmental factors and enhancing stability, productivity, and disease and pest prevention.

Investigating an Enhanced Approach for Greenhouse Climate Control: Optimising Cooling and Heating Systems

This study aimed to enhance greenhouse climate regulation by optimizing the efficiency of existing cooling and heating systems while considering external temperature and humidity conditions. We introduced an automated system capable of regulating the internal greenhouse environment, which underwent testing across 30 days in September 2021, with temperatures ranging from 26°C to 31°C and humidity from 65% to 70%. The system consistently monitored and adjusted the microclimate, with sensors capturing temperature and humidity data at 30-second intervals, amassing over 83,000 data entries for enhanced control accuracy. The automated regulation effectively maintained desired humidity, significantly reducing nighttime levels by 80% while carefully increasing daytime humidity to counteract external heat. Temperature control was largely successful, sustaining daytime levels around 32°C, but faced challenges in maintaining the target of 26°C during cooler nights. Energy consumption was optimized, with the automation leading to a significant 4-33% energy saving for cooling and an 8% saving in heating compared to traditional methods. Additionally, the system was accessible via a web interface, allowing for real-time climate tracking and prompt anomaly identification. In conclusion, the developed greenhouse automation system exhibited efficiency in equipment usage and improved temperature and humidity control. Further enhancements are required for lamp-based heating. This research contributes to the efficiency and reliability of greenhouse automation systems, mitigating risks associated with external environmental factors and enhancing stability, productivity, and disease and pest prevention.

Reinforcement Learning versus Model Predictive Control on greenhouse climate control

The greenhouse system plays a crucial role to ensure an adequate supply of fresh food for the growing global population. However, maintaining an optimal growing climate within a greenhouse requires resources and operational costs. To achieve economical and sustainable crop growth, efficient climate control in greenhouse production is paramount. Model Predictive Control (MPC) and Reinforcement Learning (RL) are the two approaches representing model-based and learning-based control, respectively. Each one has its own way to formulate control problems, define control objectives, and seek for optimal control actions that provide sustainable crop growth. Although certain forms of MPC and RL have been applied to greenhouse climate control, limited research has comprehensively analyzed the connections, differences, advantages, and disadvantages between these two approaches, both mathematically and in terms of performance. Therefore, this paper aims to address this gap by: (1) introducing a novel RL approach that utilizes Deep Deterministic Policy Gradient (DDPG) for large and continuous state–action space environments; (2) formulating the MPC and RL approaches for greenhouse climate control within a unified framework; (3) exploring the mathematical connections and differences between MPC and RL; (4) conducting a simulation study to analyze and compare the performance of MPC and RL; (5) presenting and interpreting the comparative results to provide valuable insights for the application of these control approaches in different scenarios. By undertaking these objectives, this paper seeks to contribute to the understanding and advancement of both MPC and RL methods in greenhouse climate control, fostering more informed decision-making regarding their selection and implementation based on specific requirements and constraints.

Evaluation of synthetic data generation for intelligent climate control in greenhouses

AbstractWe are witnessing the digitalization era, where artificial intelligence (AI)/machine learning (ML) models are mandatory to transform this data deluge into actionable information. However, these models require large, high-quality datasets to predict high reliability/accuracy. Even with the maturity of Internet of Things (IoT) systems, there are still numerous scenarios where there is not enough quantity and quality of data to successfully develop AI/ML-based applications that can meet market expectations. One such scenario is precision agriculture, where operational data generation is costly and unreliable due to the extreme and remote conditions of numerous crops. In this paper, we investigated the generation of synthetic data as a method to improve predictions of AI/ML models in precision agriculture. We used generative adversarial networks (GANs) to generate synthetic temperature data for a greenhouse located in Murcia (Spain). The results reveal that the use of synthetic data significantly improves the accuracy of the AI/ML models targeted compared to using only ground truth data.

A feedback control method for plant factory environment based on photosynthetic rate prediction model

In facility agriculture, environmental parameters are usually associated, uncertain and fluctuating. In the existing environmental control system, environmental parameters are regulated according to human experience, and the feedback control of environment and equipment energy consumption are not considered enough. It is of great significance to study an environmental optimization control system that can not only realize the feedback control of greenhouse climate but also reduce energy consumption, which can improve yield and reduce regulation cost. To this end, a feedback control system for plant factory environment based on photosynthetic rate prediction model is proposed in this paper. The system is composed of a master node and multiple sub-nodes. Firstly, low-power wide area network (LPWAN) technology is used to build a multi-network structure to achieve monitoring area network coverage. The master node is compatible with ZigBee, LoRa and Cat.1 communication technologies. Depending on the communication distance, ZigBee or LoRa communication technology is used for data transmission of the sub-nodes. Secondly, the photosynthetic rate prediction model was established by the least squares support vector machine (LSSVM) algorithm. Finally, the optimal control strategy for the plant factory environment was developed by integrating the multi-objective compatible control (MOCC) algorithm with a photosynthetic rate prediction model. The experimental results show that the coefficient of determination (R2) of the prediction model is 0.992. In the optimal control condition, the average leaf temperature was 27.55℃, the average CO2 concentration was 1214.20µmolmol−1, and the average PPFD was 1709.51µmolm−2s−1.

Boosting the prediction accuracy of a process-based greenhouse climate-tomato production model by particle filtering and deep learning

By generating high quality data without the big time investment and economic cost of real experiments, dynamic greenhouse climate and crop simulation models can support decisions on greenhouse climate control, crop management and greenhouse design. The reliability of simulation-based decisions depends on both the prediction accuracy and interpretability of simulation models. The prediction accuracy of these simulation models can be increased by: 1) improving mechanisms in process-based models; 2) calibrating process-based model parameters; 3) deriving black-box relationships from data. Considering the descending interpretability from (1) to (3), this study presents a knowledge-based data-driven modelling approach where firstly a process-based model is selected and modified based on domain knowledge, then data-driven improvement is applied including two steps: parameter value estimation by particle filter (PF) and further black-box improvement by deep neural networks (DNN). The approach was tested with an example of greenhouse climate-tomato production system modelling. Modules from GreenLight (Katzin et al., 2020) and TOMSIM (Heuvelink, 1995, 1996) were selected, modified and integrated into a process-based greenhouse climate-tomato model. Validation showed that PF-calibration of five greenhouse parameters decreased the seasonal relative root mean squared error (RRMSE) of indoor air vapor pressure predictions from 40.7% of that before PF-calibration to 16.4%, while it did not decrease the RRMSE of indoor air temperature predictions. Combining the PF-calibrated model with a DNN trained on a season of data decreased the RRMSE of indoor air temperature from 15.0% without DNN to 6.7%, and decreased the RRMSE of indoor air vapor pressure to 12.6%. The knowledge-based data-driven greenhouse climate-tomato model had a relative error of 0.9% for seasonal total fresh yield, and an RRMSE of 6.6% for the cumulative yield throughout the season. If process-based model parameters were not calibrated before combining the model with DNNs, the required amount and diversity of DNN training data increased because more information needed to be learnt from data by the DNNs. Without PF-calibration, combining a DNN trained on 50 days of data with the process-based model resulted in RRMSEs of 44.8% and 31.8% for indoor air temperature and vapor pressure prediction, respectively; with PF-calibration, the RRMSEs were decreased to 13.1% and 17.9%. The proposed three-step knowledge-based data-driven approach can not only improve the model prediction accuracy, but can also help to track and interpret the improvements.

MOF-801/Graphene Adsorbent Material for Greenhouse Climate Control System—Numerical Investigation

Greenhouses with efficient controlled environment offer a promising solution for food security against the impacts of increasing global temperatures and growing water scarcity. However, current technologies used to achieve this controlled environment consume a significant amount of energy, which impacts on operational costs and CO2 emissions. Using advanced metal organic framework materials (MOFs) with superior water adsorption characteristics, this work investigates the development of a new technology for a greenhouse-controlled environment. The system consists of MOF coated heat exchanger, air to air heat exchanger, and evaporative cooler. A three-dimensional computational fluid dynamics (CFD) model was developed using COMSOL software and experimentally validated for the MOF-801/Graphene coated heat exchanger (DCHE) to determine the best cycle time and power input. It was found that using desorption time of 16 min and power input of 1.26 W, the maximum water removal rate was obtained from MOF-801/Graphene of 274.4 g/kgMOF/W.hr. In addition, an overall mathematical model for the greenhouse climate control was developed and used to investigate the effects of air humidity and velocity on the input air conditions to the greenhouse. Results showed that with high relative humidity levels of 90% in the greenhouse can be conditioned to reach the required relative humidity of 50%.

Data-driven evaluation of machine learning models for climate control in operational smart greenhouses

Nowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can be grown efficiently, consuming as few resources as possible. In particular, greenhouses have proved to be an effective way of producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water use and nutrient consumption, less energy use, faster growth, and better product quality. In this article, we carry out an in-depth analysis of different machine learning (ML) models to improve climate control in smart greenhouses. As part of the analysis of the techniques we also considered 3 ways of pre-processing the data, as well as 12-hour and 24-hour forecasting. We focus on forecasting the indoor air temperature of an operational smart greenhouse, i.e. assessing the data anomalies that are inherently present in these environments due to the instability of IoT infrastructures. Several ML models are adapted to time series forecasting to provide an overview of these techniques and to find out which one performs better in this particular scenario. Our results show that, after statistically validating the results, the Random Forest Regression technique gives the best overall result with a mean absolute error of less than 1 degree Celsius.