DOI: 10.12343/Agrixiv.202607.00006  CSTR: 31252.36.agrixiv.202607.00006

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  Abstract: Xinjiang is China’s core cotton-producing region, characterized by arid climate and prominent contradiction between supply and demand of soil and water resources. The existing film-mulched drip irrigation mode in cotton fields mostly relies on farmers’ experience for irrigation, which leads to low water and fertilizer utilization efficiency, high manual management costs and insufficient dynamic regulation of irrigation quota. This paper constructs a closed-loop intelligent cotton irrigation decision-making system integrating dynamic monitoring of multi-layer soil moisture and short-term crop water demand prediction. The system collects multi-source environmental data through Time Domain Reflectometry (TDR) soil moisture sensors and integrated field micro weather stations. A Radial Basis Function (RBF) neural network is adopted to establish a 3–5 day cotton water demand prediction model, and standardized irrigation regulation schemes are automatically output combined with critical soil moisture thresholds of cotton at different growth stages. Fixed-point comparative experiments were carried out in large-scale cotton fields in Aksu, Xinjiang throughout the whole growing season. The results show that compared with the traditional empirical irrigation mode, the system can reduce field irrigation water consumption by 14%–26%, increase seed cotton yield by 22.6%, and improve water use efficiency by 76.3%. Manual input in irrigation is reduced by 72%, the comprehensive production cost of water and fertilizer drops by 12%–27%, and the additional comprehensive income per mu reaches about 196 yuan. Moreover, there is no significant deterioration in core quality indicators of cotton such as fiber length, breaking tenacity and micronaire value. Taking water saving, yield increase, cost reduction and labor saving into account, the system provides a practical intelligent technical scheme for large-scale precision irrigation of cotton in arid areas of Northwest China.

  Key words: arid area; cotton; soil moisture; intelligent irrigation; irrigation decision-making system

  Submit time: 13 July 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity.
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• ID	Submit time	Number	Download
  1	2026-07-10	10.12343/Agrixiv.202607.00006V1	Download

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