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为了提高农业用水的利用效率，实现灌区精准灌溉，对新疆阿勒泰地区福海县阔克阿尕什乡浑沃尔海种植的春小麦灌溉进行节水优化设计。根据小麦的生长环境和各生育期需水量，设计了基于BP神经网络与模糊控制的小麦灌溉系统，该系统通过田间微型气象站监测、麦田传输数据，利用BP神经网络预测出小麦需水量；以需水量和土壤实际湿度的差值和差值变化率作为模糊系统的输入量，灌溉时间作为输出量，同时将2017年人工灌溉数据与灌溉控制系统相结合利用PYTHON作对比实验，以达到精准灌溉目的。实验结果表明，BP神经网络预测效果较好，验证集R2为0.997，RPD为2.0141，满足春小麦实际需水标准。模糊控制相比于传统控制系统不会出现较大的超调量，有更好的稳定性。 BP神经网络与模糊控制灌溉系统比人工灌溉节水约23.9%，说明该灌溉系统能够提高水资源的利用效率，对实现精细化农业有着重要的参考意义。

英文摘要:

Abstract: In order to improve the efficiency of agricultural water use and achieve precision irrigation in the Fuhai County, Quoke Agashi Township, Altay region of Xinjiang, a water-saving optimization design was conducted for the irrigation of spring wheat. Based on the water requirements of wheat during different growth stages and its environmental conditions, an irrigation system for wheat was designed using BP neural networks and fuzzy control. The system utilizes data from on-field micro-weather stations and wheat field sensors to predict the water requirements of wheat through BP neural networks. The difference and rate of change of the difference between water requirements and actual soil moisture are used as input parameters for the fuzzy system, with irrigation time as the output parameter. In comparison experiments, 2017 manual irrigation data were combined with the irrigation control system using PYTHON to achieve precise irrigation. Experimental results show that the BP neural network has good predictive performance, with a validation set R2 of 0.8539 and RPD of 2.0141, meeting the actual water requirements of spring wheat. Compared to traditional control systems, fuzzy control exhibits better stability with minimal overshooting. The BP neural network and fuzzy control irrigation system save approximately 23.9% more water compared to manual irrigation, demonstrating its ability to improve water resource utilization efficiency and its significant reference value for precision agriculture.

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