Neugebauer Maciej, Akdeniz Cengiz, Demir Vedat, Yurdem Hüseyin
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland, Oczapowskiego, 10-719.
Ege University of Izmir, Campus 35100, Bornova, Izmir, Turkey.
Sci Rep. 2023 Oct 28;13(1):18485. doi: 10.1038/s41598-023-45203-2.
A two-dimensional finite element (FEM) model was developed to simulate water propagation in soil during irrigation. The first dimension was water distribution depth in soil, and the second dimension was time. The developed model was tested by analyzing water distribution in a conventional (clock-controlled) irrigation model. The values in the conventional model were calculated based on the literature. The results were consistent with the results obtained from the model. In the next step, a fuzzy logic model for irrigation control was developed. The input variables were ambient temperature, soil moisture content and time of day (which is related to solar radiation and evapotranspiration), and the output variable was irrigation intensity. The fuzzy logic control (FLC) model was tested by simulating water distribution in soil and comparing water consumption in both models. The study demonstrated that the depth of the soil moisture sensor affected water use in the fuzzy logic-controlled irrigation system relative to the conventional model. Water consumption was reduced by around 12% when the soil moisture sensor was positioned at an optimal depth, but it increased by around 20% when sensor depth was not optimal. The extent to which the distribution of fuzzy variables affects irrigation performance was examined, and the analysis revealed that inadequate distribution of fuzzy variables in the irrigation control system can increase total water consumption by up to 38% relative to the conventional model. It can be concluded that a fuzzy logic-controlled irrigation system can reduce water consumption, but the system's operating parameters should be always selected based on an analysis of local conditions to avoid an unintended increase in water use. A well-designed FLC can decrease water use in agriculture (thus contributing to rational management of scarce water resources), decrease energy consumption, and reduce the risk of crop pollution with contaminated groundwater.
开发了一个二维有限元（FEM）模型来模拟灌溉期间土壤中的水分传播。第一个维度是土壤中的水分分布深度，第二个维度是时间。通过分析传统（时钟控制）灌溉模型中的水分分布来测试所开发的模型。传统模型中的值是根据文献计算得出的。结果与从该模型获得的结果一致。在下一步中，开发了一种用于灌溉控制的模糊逻辑模型。输入变量为环境温度、土壤湿度含量和一天中的时间（与太阳辐射和蒸散有关），输出变量为灌溉强度。通过模拟土壤中的水分分布并比较两个模型中的用水量来测试模糊逻辑控制（FLC）模型。研究表明，相对于传统模型，土壤湿度传感器的深度会影响模糊逻辑控制灌溉系统中的用水情况。当土壤湿度传感器位于最佳深度时，用水量减少了约12%，但当传感器深度不理想时，用水量增加了约20%。研究了模糊变量分布对灌溉性能的影响程度，分析表明，灌溉控制系统中模糊变量分布不当会使总用水量相对于传统模型增加高达38%。可以得出结论，模糊逻辑控制灌溉系统可以减少用水量，但该系统的运行参数应始终根据当地条件分析来选择，以避免意外增加用水量。精心设计的FLC可以减少农业用水（从而有助于合理管理稀缺水资源）、降低能源消耗，并降低作物受污染地下水污染的风险。
