E. Fitz-Rodríguez

Yield Prediction and Growth Mode Characterization of Greenhouse Tomatoes with Neural Networks and Fuzzy Logic

Despite the technological advances implemented in greenhouse crop production, greenhouse operation relies on human expertise to decide on the optimum values of each environmental control parameter. Most importantly, the selected values are determined by human observation of the crop responses. Greenhouse tomatoes often show a pattern of cycling between reproductive and vegetative growth modes. The growth mode is a practical visual characterization of the source-sink relationships of the plants resulting from the greenhouse environment (aerial and root zone). Experienced greenhouse tomato growers assess the growth mode based on morphological observations, including quantitative (length, diameter, elongation rates) and qualitative (shape and color) features of the plant head, stems, flowers, trusses, and leaves. Data from greenhouse environments and crop records from an experimental production in Tucson, Arizona, and from a large-scale commercial operation in Marfa, Texas, were used for modeling the growth mode of tomato plants with fuzzy logic. Data from the commercial operation were used to model weekly fluctuations of harvest rate, fruit size, and fruit developing time with dynamic neural networks (NN). The NN models accurately predicted weekly and seasonal fluctuations of the fruit-related parameters, having coefficients of determination (R2) of 0.92, 0.76, and 0.88, respectively, for harvest rate, fruit fresh weight, and fruit developing time, when compared with a dataset used for independent validation. The fuzzy modeling of growth mode allowed discrimination of the reproductive and balanced growth modes in the experimental system, and modeling of the seasonal growth mode variation in the commercial application. Both modeling results might be applicable to commercial operations for making decisions on greenhouse climate control and overall crop management practices.

Dynamic Response and Environmental Uniformity of a Naturally Ventilated Greenhouse Cooled with a Variable-Pressure Fogging System

Greenhouse crop production systems have been established throughout the world, including arid and semi-arid regions, to fulfill a market demand of locally grown produce consistently through the year. In these particular regions while they have the advantage of sunshine year-round, production during the summer is a challenge due to elevated air temperatures. Fog systems have proven to be a good economical alternative for evaporative cooling while potentially providing a more uniform environment when compared to fan and pad systems. High-pressure fogging systems equipped with variable frequency drives can be operated at different pressures to meet the varying cooling demands during the day. This feature adds the flexibility of varying the fog flow rate by operating at lower pressures or by changing the number of working fog lines accordingly to the cooling demands. These systems may offer the potential advantage of energy and water saving by operating at a low frequency while providing the proper amount of fog accordingly to the cooling loads. A variable pressure fogging systems operating in the range of 4.8 to 10.3 MPa (700 to 1500 psi) was recently installed in a greenhouse at the University of Arizona Controlled Environment Agriculture Center (UA-CEAC) for the purpose of developing advanced control strategies for optimum greenhouse environments. This study experimentally evaluated the dynamics of air and canopy temperatures, crop evapotranspiration rates, and climate uniformity in the greenhouses working under various fogging system operational pressures and greenhouse side/roof vent opening configurations.