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.

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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

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article

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

Y F LIU, Fang Zhou, Min Tian, Kaiwen Huang et al.
Smart Agricultural Technology
Smart Agriculture and AI
article

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

Y F LIU, Fang Zhou, Min Tian, Kaiwen Huang, Yaming Sheng, Xiang Liu, Leifeng Guo, Cheng Peng
article en

Abstract

• 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.

Smart Agricultural TechnologyVol. 14
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)
Shihezi University
Life in Land
Openalex Percentile: Top 15%
Smart Agriculture and AI
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