A decision-support framework for pre-emergence soil moisture management in cotton fields: Lightweight dual-domain segmentation of moisture-condensed mulch film via UAV imagery
• Explores the potential correlation between moisture-condensed mulch film and subsurface soil moisture conditions. • Proposes a spectral threshold–guided automatic mask labeling method (STAML). • Develops a lightweight dual-domain fusion semantic segmentation network (DDF-SEGNet). • Examines the spatial distribution trend of moisture-condensed mulch film across different irrigation zones based on single-temporal observations. In arid and semi-arid regions, pre-emergence soil moisture critically influences cotton seed germination, yet the lack of a crop canopy limits the applicability of conventional remote sensing monitoring. This study investigates Moisture-condensed mulch film as a research subject, exploring it may correlate with subsurface soil moisture conditions. Based on UAV multispectral imagery, a technical pipeline integrating a Spectral-Threshold-Guided Automatic Mask Labeling (STAML) method and a lightweight Dual-Domain Fusion Segmentation Network (DDF-SEGNet) was developed to achieve intelligent recognition of Moisture-condensed mulch film and sub-main pipes. Specifically, STAML leverages spectral threshold characteristics to generate initial masks, significantly reducing the manual annotation burden; DDF-SEGNet adopts a "spatial-dominant, frequency-auxiliary" collaborative architecture, demonstrating favorable applicability in the segmentation task. In experiments conducted in Xinjiang’s arid cotton fields, the model achieved favorable segmentation performance with only 0.43M parameters (Dice = 0.956, mIoU = 0.940) and an inference speed of 168.79 FPS, suggesting its preliminary feasibility for edge-computing deployment. Based on the segmentation results in different irrigation zones from a single-temporal flight, this study suggests Moisture-condensed mulch film’s descriptive spatial distribution trends. These findings suggest that, in arid regions, the spatial distribution of Moisture-condensed mulch film may correlate with subsurface soil moisture status, offering a potential exploratory perspective for monitoring soil moisture during critical early growth stages.
Authors
- Y F LIU (ORCID: https://orcid.org/0000-0001-7074-0272)
- Fang Zhou (ORCID: https://orcid.org/0000-0003-3642-3921)
- Min Tian (ORCID: https://orcid.org/0009-0006-7731-0113)
- Kaiwen Huang
- Yaming Sheng
- Xiang Liu
- Leifeng Guo
- Cheng Peng
Institutions
- Shihezi University (CN)
- Xinjiang Agricultural University (CN)
- Westlake University (CN)
- Agricultural Information Institute (CN)
- Ministry of Education (KR)
- Chinese Academy of Agricultural Sciences (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- Xinjiang University (CN)
Publication Details
- Journal
- Smart Agricultural Technology
- Published
- 2026-04-16
- DOI
- https://doi.org/10.1016/j.atech.2026.102117
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shihezi University