Abstract
In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.
Highlights
As a big agricultural country, China faces a severe shortage of agricultural irrigation water, owing to the low per-capita water resources and the imbalance between water supply and demand [1, 2]
The intelligent management and scientific decision-making of agricultural irrigation is premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions [6,7,8]
After analyzing the water supply and consumption features in local irrigated areas, Sun et al [9] adjusted the weights of the indices in the current water consumption evaluation system through analytic hierarchy process (AHP) and constructed a time series prediction model to forecast the available water resources, industrial water demand, domestic water demand, and agricultural water demand in the irrigated areas with different assurance rates, thereby balancing the water supply with water demand in these areas
Summary
As a big agricultural country, China faces a severe shortage of agricultural irrigation water, owing to the low per-capita water resources and the imbalance between water supply and demand [1, 2]. Computational Intelligence and Neuroscience replaced the ordinary convolution and rectified linear unit (ReLU) of the U-Net semantic segmentation network with depthwise separable convolution and Mish activation function, respectively, thereby improving the classification accuracy of the planting areas of different crops They constructed an inversion model for the water content in the soil at different depths, which couples the normalized difference vegetation index (NDVI) and automatic water extraction index. Let Γ be the slope between the temperature change curve and the saturated vapor pressure; FS the net radiation of crop surface; TH the heat flux of the soil in the irrigated area; δ the psychrometer constant; TAV the mean temperature; vf the wind speed at a fixed height; and AVB and AVR saturated and actual vapor pressures, respectively. Suppose the height is fixed at 2 m. en, the mean wind speed can be calculated by vf vcln(67.48.c9−
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