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Introduction: Medium and low technology greenhouses use natural ventilation as a method of temperature and humidity control. However, at certain times of the year, this is insufficient to extract excess heat inside the greenhouse, so devices such as hydrophanes (humidifiers) have been implemented to reduce the temperature. It is necessary to know the behavior of temperature and humidity, since both factors influence the development of crops and, therefore, their yield. Objective: To develop a computational fluid dynamics (CFD) model of a naturally ventilated zenithal greenhouse equipped with hydrophanes to understand the spatial and temporal distribution of temperature and humidity inside the greenhouse. Methodology: The experiment was carried out in a greenhouse equipped with hydrophanes and grown with bell pepper. Temperature and humidity measurements were performed from March 7 to 25, 2014. The ANSYS Workbench program was used for the 3D CFD modeling. Results: The CFD model satisfactorily described the temperature and humidity distribution of the greenhouse, with an error of 0.11 to 3.43 °C for temperature, and 0.44 to 10.80 % for humidity. Limitations of the study: Numerical modeling using CFD is inadequate to model the temporality of the variables. Originality: There are few studies that model humidity behavior with CFD and the use of hydrophanes in Mexico. Conclusions: The CFD model allowed visualizing the distribution of temperature and air humidity inside the greenhouse.

One of the advantages of controlled agriculture is the notable increase in crop yields. However, at high-tech levels, water use productivity has increased, while energy use productivity has decreased. Therefore, the objective of this study was to estimate water, energy and labor use productivity in tomato cultivation, for a low-tech greenhouse. The inputs used in the production process were recorded from April 24 (date of transplant) to October 16, 2016 (date of last harvest). The yields obtained were 19.07 kg∙m-2. Water use productivity was 27.86 kg∙m-3 (35.89 L∙kg-1), which is within the range reported for unheated plastic greenhouses (30-40 L∙kg-1). Energy productivity was 0.331 kg∙MJ-1, and the energy consumed per unit area was 57.61 MJ∙m-2, with Calcium nitrate being the input with the highest energy demand (49.49 %). During the production cycle, 738 working hours were used, 78 % of which were dedicated to cleaning and maintenance, with labor productivity of 3.02 kg∙h-1.

The objectives of this research were two: first to investigate experimentally the behavior of pineappl (Ananas comosus, L.) thin layer drying in a greenhouse-type solar dryer and second to describe the best fitting kinetic and mathematical model taken from literature. A large scale greenhouse dryer designed and installed at Silpakorn University, Nakhon Pathom, Thailand was used to dry slices 1 cm width at temperature range between 25-60 °C with relative humidity between 50-90%. Nine statistical models, either empirical or semi-empirical, were tested in order to validate the experimental data. A non-linear regression analysis conducted by a statistical computer program was applied to evaluate the constants of all the models. The parameter values, root mean square error (RMSE), mean absolute error (MAE) and modelling efficiency (EFF) of the nine models were calculated. Comparison outcomes of two experiments are displayed between the predicted moisture content and the observed pineapple moisture content. Hasibuan and Daud drying model proved to describe the best pineapple solar drying curves. The two experiments were carried out on sunny days, the second experiment on the third day showed cloudiness decreasing the solar radiation. Mathematical models of pineapple drying in a greenhouse dryer have not been found so far in the literature. Drying curves obtained from experiments showed that the constant drying and the falling drying rate periods exist. Nine thin-layer drying models were fitted to two experimental data in order to describe the drying characteristics of pineapple founding that the Hasibuan and Daud model was the best fitting.

The HortSyst model is a new discrete time model for describing the dynamics of photo-thermal time (PTI), total dry matter production (DMP), N uptake (Nup), leaf area index (LAI), and evapotranspiration (ETc) for greenhouse crops. The first three variables are considered as state variables and the latter two are conceptualized as output variables. This model was developed as a tool for decision support systems in Mexican greenhouses for the application of N and water in tomato (Solanum lycopersicum L.) production. The HortSyst has 13 parameters. It was used to calibrate the model and estimate the correct parameter values for the crop season. An experiment was carried out to test model predictions in a greenhouse during the autumn-winter season in Chapingo, Mexico. Tomato ‘CID F1’ was grown in a hydroponic system and plants were distributed with a density of 3.5 plants m-2. The tomato crop was transplanted on 21 August 2015. A weather station was installed inside the greenhouse to measure temperature, relative humidity, and global radiation. The HortSyst model provides an excellent predictive quality for DMP, Nup, LAI, and ETc according to the statistics. Values for bias (BIAS) were DMP (-3.897), Nup (-0.071), LAI (0.026), and ETc (3.647), values for root mean square error (RMSE) were DMP (14.543), Nup (0.500), LAI (0.100), and ETc (39.330), and values for modeling efficiency (EF)were DMP (0.996), Nup (0.991), LAI (0.998), and ETc (0.815). The model proposed and described in this paper can be integrated as a decision support tool for N supply and irrigation management in greenhouse production systems.

Introduction: Mathematical crop modeling allows selecting different simulation environments and analyze the response of different variables over time to estimate, predict and potentiate the growth of a crop. Objective: To simulate and evaluate a dynamic mathematical model to predict tomato growth and nitrogen content in the plant under greenhouse conditions. Methodology: The simulation was performed with the VegSyst model, for which climate variables and greenhouse tomato crop variables were recorded during two cycles, for the evaluation a local sensitivity analysis and non-linear parametric identification were applied. Results: The sensitivity analysis allowed identifying the most sensitive parameters of the model and with the parametric identification the values of the parameters were found, which allowed fitting the simulations to the measurements made in the greenhouse. Limitations of the study: The VegSyst model only allows simulating nitrogen in the plant. In addition, it is necessary to calibrate the model to the climatic conditions of the site for experiments in other areas. Originality: Based on the VegSyst model, equations were proposed and calibrated to estimate height, number of fruits, nodes, dry matter of fruits and dry weight of fruits. Conclusions: The model predicts the growth of the tomato crop and the nitrogen content in the plant under greenhouse conditions.

Control of the environmental conditions inside a greenhouse is fundamental to obtain high yields and product quality. One of the tools to improve this control is the mathematical modeling of the system in question. Therefore, in this work a dynamic energy balance model was developed with the objective of predicting the air temperature inside a greenhouse. The model considers the process of plant transpiration, ventilation and condensation inside a greenhouse, outside climate conditions, crop characteristics (leaf area index, stomatal and aerodynamic resistance), cover properties and greenhouse characteristics. The information used was collected in a greenhouse located at the Universidad Autonoma Chapingo, Mexico (19° 29’ LN and 98° 53’ LW), with a polyethylene cover, natural ventilation and tomato cultivation. The model was calibrated in order to obtain optimal values for six parameters: infiltration coefficient, heat transfer coefficient through the cover, heat transfer coefficient through the soil, crop-specific transpiration and wind effect coefficient. After calibration, the efficiency of the model improved by 33.84 %. The performance of the model was quite acceptable when evaluated with another set of data.

In dynamic modeling of the greenhouse climate, prediction errors are a significant issue due to uncertainties in initial state values, input variables, model parameters and model structure, all propagating in time in a nonlinear way. We investigated a data assimilation approach using two non-linear Kalman filters in light of prediction uncertainty. An extended (EKF) and an unscented (UKF) Kalman filters were designed to estimate climate states, and also both the states and model parameters. The states to be estimated were air temperature, absolute humidity and carbon dioxide concentration inside a greenhouse. Year round measurements from a Dutch greenhouse with a rose crop were used. The dynamic model was first calibrated manually by estimating ten of its parameters. Uncertainties of the measurements needed for designing EKF and UKF were specified via literature sources whereas the uncertainties related to the process were tuned. Both filters increased the model predictive power several orders of magnitude with respect to mean squared error (MSE) statistics and one order of magnitude with respect to mean absolute error (MAE) analyzed during autumn-winter and spring-summer seasons when only the model states were estimated. However, no improvement on the one step ahead state predictions were achieved when both states and model parameters were estimated by both nonlinear filters. Results showed that data assimilation based on nonlinear Kalman filters is advantageous over data assimilation that uses only model calibration. Therefore, improved model of the greenhouse climate by data assimilation can be used in controlling and optimizing more efficiently the greenhouse system.

Sun-drying is the most widely used method of preserving agricultural products; however, under hostile climatic conditions it leads to serious losses in product quantity and quality. The use of solar drying technology in developing countries can reduce postharvest losses and significantly improve product quality compared to other methods. The development of mathematical models of solar dryers is necessary to be able to design, control and optimize this type of system. The aim of this study is to provide an overview of the different approaches to modeling and simulating greenhouse solar dryers, both of natural and forced convection. In addition, models that have not been extensively used in this area, such as empirical and numerical ones, are discussed.

Semi-closed greenhouses are more efficient in water and energy use than conventional ones. However, one of the challenges is the humidity control. This paper is based on experimental data generated during the 2012 tomato production cycle in a semi-closed greenhouse located at the Humboldt University of Berlin. The aim was to generate two models for simulation of the humidity behavior. In the first model, the greenhouse was completely closed and only transpiration and condensation processes were taking place. In this model, after calibration, a mean absolute error (MAE) of 1.94 and a correlation coefficient (r) of 0.87 were obtained, with the boundary layer resistance and the radiation extinction coefficient being the most varying parameters. The second model, in addition to the processes considered in the first, had natural ventilation, so a sensitivity analysis was performed before calibration, resulting in a MAE = 2.17 and r = 0.85, with the latent heat of vaporization and the radiation extinction coefficient being the most sensitive parameters. The developed models allow for a quantitative comparison of the processes involved in the water balance in the open and closed system, and will thus enable us to improve control over this factor. Both models were implemented using Simulink in Matlab®.

The possibility of improving the accuracy of climate monitoring in greenhouse cultivation by way of model based filtering was explored. The focus was on estimating the average climate inside a greenhouse compartment. Starting point was employing an extended Kalman filter (EKF), combined with a greenhouse climate differential equation model. In two different greenhouses (A and B), temperature and humidity were monitored with a 5-min sampling resolution with a sensor grid. The available data sets spanned 1 and 0.5 years. With the average over all sensors as reference signal, the root mean squared errors (RMSEs) of the unfiltered signals (coming from single sensors) were 0.43 °C and 0.48 g m −3 for greenhouse A, and 0.80 °C and 0.64 g m −3 for greenhouse B. The filter was compared with a moving average (MA) filter, and an unscented Kalman filter (UKF). Overall, monitoring accuracy was not improved by any of the filters, and in most cases it deteriorated. Performance was strongly linked to the choice of filter, where the EKF outperformed the other filters by a considerable difference. The violations on the assumptions of whiteness and normality of the noise were severe but had a moderate effect on the RMSEs (0.11 °C and 0.10 g m −3 for greenhouse A). A clear link was found between model accuracy and monitoring accuracy. A 10–15 fold decrease of state errors was associated with an RMSE reduction down to 0.1 °C and 0.1 g m −3 , the expected equivalent of increasing the number of climate sensors from 1 to 25.

Due to the lack of information about energy consumption in heating and cooling requirements ingreenhouses, the enthalpy was used as an approach of the energy content in the environment. A sensitivity analysis was done through seven Neural Network models for enthalpy prediction. It was found that the most important variables to explain the energy content in the greenhouse are temperature, transpiration, and heating and cooling systems, which correspond to inputs of model 4 with a MSE equal to 0.38 and 1.28 for one and two times ahead, respectively. Temperature and relative humidity data were collected from Santa Rosa, Sinaloa, from 1999 to 2009. The ellipses for the comfort zone were constructed in order to determine how far the external conditions are comparable with the optimal conditions for tomato production, and also to estimate the amount of energy required in each case to reach the comfort zone. Data analysis showed that most of the time those external conditions are below and right from the comfort zone, which pointed out a problem of high temperature and low relative humidity. Besides, the most critical months for tomato production are from July to September because the external conditions are far away from comfort zone, Therefore, it is recommended to dedicated those months for cleaning the greenhouse.

Evolutionary Algorithms, Bio-inspired Algorithms and Hybrid Algorithms, considered as Artificial Intelligence techniques have gained great importance due to their capacity to solve complex and nonlinear problems that conventional methods cannot solve. In the last decade the application of these techniques has been applied to the development of intelligent algorithms to identify, predict, optimize and control the weather for crops in protected environments (greenhouses) and to generate near optimal climatic conditions for biosystems for growth and crop development, and to obtain higher yields and product quality. The current paper shows a review of Artificial Intelligence Techniques applied in agricultural engineering, mainly to modeling and control of greenhouse environment, especially those algorithms inspired by the collective behavior of biological systems, evolution and natural genetics.

The current research was conducted in a greenhouse of 20 m2 area located at the University of Chapingo, Mexico, in hydroponic system (NFT) with recirculating nutrient solution, three treatments were applied (100, 150 and 200 ppm) of calcium concentration to study effects on fruit quality and yield of tomato (Lycopersicum esculentum Mill.). The experiment was conducted from September 30, 2010 to February 4, 2011 and random samples of fruits were collected for three consecutive weeks, where each week were harvested fruits of the first to the third cluster of each plant. Treatment two showed the highest yield and it was obtained in the second cluster with 57.1 %, in treatment one on the third week was 37.5 % and yield treatment three during the second and third week was 38.6 % with respect to the total. The fruits quality was good because tomatoes showed good acidity and salt content as recommended. However the sugar content was low and statistically different compared with previous reported studies. The average yield was low and different in all cases due to the effect of extreme temperatures outside the range of 10-25 °C and these results cannot be asigned to the concentration of calcium because there were not significant differences among the treatments.

We investigated the effect of model reduction and data assimilation on prediction accuracy of greenhouse climate. For this, a first-principle model was reduced, and calibrated with measurement data. Calibration data consisted of a time series of temperature, humidity, and carbon-dioxide concentration in a rose greenhouse, together with 15 variables related to outside climate and control actions. The results indicate that model reduction does not produce a crucial loss of prediction accuracy. In contrast, data assimilation decreases the size and variance of prediction errors drastically, making predictions much more reliable. A static linear model seems to predict the most essential input-output response for temperature and humidity, but the predictive power for carbon-dioxide concentration is limited. The prediction errors have standard deviations of typically 2°C, for temperature, 5-10% for relative humidity, and 200-300 ppm for CO2. The prediction errors have biases of the same order, which differ per period for which the predictions are made. We believe these results are promising for modelling climate via a static black box approach, in combination with data assimilation. The relatively low computational demand for uncertainty analysis and easy model building provide a suitable starting point for investigating augmented systems, such as plant development based on controlled climate.

This research was conducted in a Plant Science Department greenhouse at the Universidad Autonoma Chapingo, located in Chapingo, Mexico State. We evaluated three irrigation frequencies (3, 4 and 5 irrigations per day), which were applied using an automated drip irrigation system at three depths (7, 14 and 25 mL•day-1) controlled by drippers; tezontle was used as substrate. A factorial arrangement of two factors, each at three levels, was used in a completely randomized design, and Steiner universal solution (90 mL•day-1) was used to provide nutrition for the production of Calla lily cv Reno. Crop behavior was analyzed in terms of number, height and development time of flowers and leaves. The crop was established from September 2010 to January 2011, and data taking was weekly from four to fifteen weeks after planting the rhizomes, when the number of developed leaves and flowers were quantified and subsequently the height of both was measured. With the data obtained, we proceeded to analyze the effect of both factors, which determined that the factor affecting the number, height and development time for Calla lily cv Reno flowers is irrigation frequency. With four and five daily irrigations, a greater number of flowers develops than with three.

This paper presents a comparison of different evolutionary algorithms (EAs), such as Genetic Algorithms (GAs), Evolutionary Strategies (ES) and Evolutionary Programming (EP) to calibrate parameters of a climate model that describes the behaviour of air temperature and relative humidity (RH) within a greenhouse where a tomato crop is being grown. The objective was to determine which method generates parameter values that give the best prediction of the environment of a greenhouse located in the central region of Mexico. Simulation and analysis of the climate model show that the estimations of the inside temperature and RH are closest to the measurements when EP was used to calibrate the parameters of the greenhouse model.

Because of the rapid growth of the greenhouse industry in Mexico and the increasing need for relevant information about structural design and management, the internal microclimate of a naturally ventilated greenhouse in central Mexico was investigated. In order to quantify the ventilation rates resulting from inside and outside environmental conditions, tracer gas (TG) experiments were carried out. Properties of an insect screen were determined by a series of wind tunnel experiments. The same experimental conditions were then used to calculate the air exchange rate by means of computational fluid dynamics (CFD). The results showed a significant correlation between ventilation rate and wind speed (R2 = 0.7079). For instances when only roof vents are kept open, high wind speeds (>4.5 m s-1) are required to provide a recommended air exchange rate of one greenhouse volume per minute (6.64 × 10-2 m3 m-2 s-1). The correlation between CFD-simulated and TG-measured ventilation rates was 0.7065 at wind speeds higher than 2 m s-1.

The most extended control in the greenhouse industry across the world is the classical Proportional-Integral-Derivative control (PID).

Computational fluid dynamics is a tool that has been used in recent years to develop numerical models that improve our understanding of the interaction of variables that make up the climate inside greenhouses. In the past five years, more realistic studies have appeared due mainly to the development of more powerful software and hardware. However, it is necessary to perform an analysis to show us the trends, strengths and weaknesses in the use of this tool. In this study, we reviewed the state of the art of CFD in studies of airflow and climate inside greenhouses, analyzing the most important issues that help us understand how it has evolved, as well as trends and limitations on their use. Key words: Airflow, CFD, greenhouse, turbulence, porous media.