Corn is an important part of the Mexican diet. The crop requires constant monitoring to ensure production. For this, plant density is often used as an indicator of crop yield, since knowing the number of plants helps growers to manage and control their plots. In this context, it is necessary to detect and count corn plants. Therefore, a database of aerial RGB images of a corn crop in weedy conditions was created to implement and evaluate deep learning algorithms. Ten flight missions were conducted, six with a ground sampling distance (GSD) of 0.33 cm/pixel at vegetative stages from V3 to V7 and four with a GSD of 1.00 cm/pixel for vegetative stages V6, V7 and V8. The detectors compared were YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l, and YOLOv5 versions s, m and l. Each detector was evaluated at intersection over union (IoU) thresholds of 0.25, 0.50 and 0.75 at confidence intervals of 0.05. A strong F1-Score penalty was observed at the IoU threshold of 0.75 and there was a 4.92% increase in all models for an IoU threshold of 0.25 compared to 0.50. For confidence levels above 0.35, YOLOv4 shows greater robustness in detection compared to the other models. Considering the mode of 0.3 for the confidence level that maximizes the F1-Score metric and the IoU threshold of 0.25 in all models, YOLOv5-s obtained a mAP of 73.1% with a coefficient of determination ($R^2$) of 0.78 and a relative mean square error (rRMSE) of 42% in the plant count, followed by YOLOv4 with a mAP of 72.0%, $R^2$ of 0.81 and rRMSE of 39.5%.

Vegetables cultivated in cities, carry the risk of absorbing pollutants generated by anthropogenic activities, increasing the health risk of those who consume them. Heavy metal concentrations (As, Cd, Cr, Ni and Pb) were analyzed in water, soil and in plant parts of lettuce (roots and leaves) and cherry tomatoes (roots, stem, leaves and tomato fruit) lettuce and cherry tomatoes, cultivated in urban gardens in Mexico City. In none of the studied sites was the heavy metal concentration in the soil above the permitted limits. In irrigation water, Cr surpassed the permitted limit in 5 sites and that of Cd in one site. Ni surpassed the established limit for lettuce and cherry tomato in the rank among 0.7 and 31.15 mg kg-1 in 9 sites, Cr exceeded the allowed limits 1.45-38 mg kg-1 in 11 sites, As go beyond the established limits between 5-52 mg kg-1 in 4 sites and to a lesser extent, Pb was above the allowed limits between 15.5 and 26 mg kg-1. According to the translocation factor, lettuce and cherry tomato could be an important phytoextractors of Cr and Cd respectively. The compound risk value for lettuce (TTHQ) was greater than one in three sites for lettuce and five sites for cherry tomato. Also, in gardens A, D, E and J, the Hazard Index (HI) showed a potential risk (HI>1), which reveals the danger of consuming both vegetables given the conditions of soil, water and air and taking into account all paths of exposure.

Sensitivity analysis is the first step in elucidating how the uncertainties in model parameters affect the uncertainty in model outputs. Calibration of dynamic models is another issue of considerable interest, which is usually carried out by optimizing an objective function. The first aim of this research was to perform a global sensitivity analysis (GSA) with Sobol’s method for the 16 parameters of the new HORTSYST nonlinear model that simulates photo–thermal time $\left(PTI\right)$, daily dry matter production $\left(DMP\right),$ nitrogen uptake $\left(Nup\right)$, leaf area index $\left(LAI\right)$, and crop transpiration $(E{T}_c)$. The second objective was to carry out the calibration of the HORTSYST model by applying a differential evolution (DE) algorithm as the global optimization method. Two tomato (Solanum lycopersicum L.) crops were established during the autumn–winter and spring–summer seasons under greenhouse and soilless culture conditions. Plants were distributed with a density of 3.5 plants m⁻². Air temperature and relative humidity were measured with an S-THB-M008 model sensor. Global solar radiation was measured with an S-LIB-M003 sensor connected to a U-30-NRC datalogger. In the sensitivity analysis run in the two growth stages, it was observed that a greater number of parameters were more important at the beginning of fructification than at the end of crop growth for 10% and 20% of the variation of the parameters. The sensitivity analysis came up with nine parameters ($RUE$, $a$, $b$,$c_{1 }$,$c_2$,$A$, $B_d$, $B_n$, and $PTIini$) as the most important of the HORTSYST model, which were included in the calibration process with the DE algorithm. The best fit, according to $RMSE$, was for $LAI$, followed by $Nup$, $DMP$, and $E{T}_c$ for both crop seasons; the $RMSE$ was close to zero, indicating a good prediction of the model’s performance.

Modeling and simulation have become fundamental tools for the microclimatic analysis of greenhouses under various climatic conditions. These models allow precise control of the climate inside the structures and the optimization of their performance under any situation. In Colombia, the availability of energy balance models adapted to local greenhouses and their climate is limited, which affects the decision-making of both technical advisors and growers. This study focused on calibrating and evaluating a dynamic energy balance model to predict the thermal behavior of an innovative type of plastic-covered greenhouse designed for the Bogotá savanna. The selected model considers fundamental heat and mass transfer processes, incorporating parameters that depend on the architecture of the structure and local climatic conditions, making it suitable for protected agriculture in Colombia. The results of the post-calibration evaluation showed that the model is highly accurate, with a temperature prediction efficiency close to 86%. This ensures that the model can accurately predict the thermal behavior of the greenhouse being evaluated. It is important to note that the model can also anticipate phenomena characteristics of Colombian greenhouses, such as thermal inversion. This advance has become a valuable tool for decision-making in protected agriculture in the region.

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop.