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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.

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.

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.

Cooling is an important practice that extends the shelf life of fruit and vegetable products. The arrangement of these products for cooling is similar to a macroporous bed, where a cold fluid circulates between packed particles. The study of these systems is essential to estimate characteristic operation variables so that they can be suitably designed. The aim of this study was to evaluate the use of the Ergun equation in estimating the pressure drop in a forced-air cooling unit. As study geometry, a pipe 30.0 cm long and 15.5 cm in diameter, which was filled with Styrofoam spheres and subjected to experimental evaluation of pressure drop (ΔPexp) with various sphere diameters and mass air flux, was used. Void fraction, temperature and air density and viscosity were evaluated in each condition. The pressure drop was calculated with the Ergun equation (ΔPest) and the parameters of this model were calibrated through the MatLab® lsqnonlin.m subroutine, with which the adequate estimation of the packed bed was obtained. Based on geometry of packed objects, the results are applicable to fruit such as apples, oranges, peaches, lemons and plums, among others.

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.

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.