DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net

Authors

Wei Tian (ORCID: https://orcid.org/0000-0002-3007-3740)
Kang Chong (ORCID: https://orcid.org/0000-0003-4364-778X)
Jingyu Zhang (ORCID: https://orcid.org/0009-0003-9810-0455)

Institutions

Beijing Botanical Garden (CN)
Institute of Botany (CN)
University of Chinese Academy of Sciences (CN)
China National Botanical Garden

Publication Details

Journal: aBIOTECH
Published: 2025-08-22
DOI: https://doi.org/10.1007/s42994-025-00235-2
Citations: 1
Primary Topic: Smart Agriculture and AI
Type: article
Field-Weighted Citation Impact: 1.75

Funders

Commonwealth Scientific and Industrial Research Organisation
Chinese Academy of Sciences

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article

DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net

Wei Tian, Kang Chong, Jingyu Zhang

Aug 22, 2025

1 citations

aBIOTECH

Smart Agriculture and AI

1.75

article

DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net

Wei Tian, Kang Chong, Jingyu Zhang

August 22, 2025article en

1 citations

Abstract

No abstract available for this paper.

aBIOTECHVol. 6(4)

Beijing Botanical Garden (CN), Institute of Botany (CN), University of Chinese Academy of Sciences (CN), China National Botanical Garden

Commonwealth Scientific and Industrial Research Organisation, Chinese Academy of Sciences

Openalex Percentile: Top 13%

Smart Agriculture and AI

1.75

DOI Open Access PDF OpenAlex

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DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net — Wei Tian, Kang Chong, et al. · aBIOTECH (2025) | TGRS Research Map | TGRS