UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model.

Jiaxin Gao, Feng Tan, Zhaolong Hou, Xiaohui Li, Ailin Feng, Jiaxin Li, Feiyu Bi

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Article in Plants (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Weed Discrimination at the Seedling Stage in Dryland Fields Under Maize-Soybean Rotation.Plants (Basel, Switzerland) · 2026
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7 authors.

Jiaxin GaoCollege of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.ORCID 0009-0009-1675-7669

Feng TanCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Zhaolong HouCollege of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Xiaohui LiCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Ailin FengCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Jiaxin LiCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Feiyu BiCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. This phenomenon has a direct impact on the rice yield. In the field environment, the existing methods for detecting missing seedlings based on unmanned aerial vehicle (UAV) remote sensing images often have unsatisfactory effects. Therefore, to enable the fast and accurate detection of missing rice seedlings and facilitate subsequent reseeding, this study proposes a UAV remote-sensing-based method for detecting missing rice seedlings in large fields. The proposed method uses an improved PCERT-DETR model to detect rice seedlings and missing seedlings in UAV remote sensing images of large fields. The experimental results show that PCERT-DETR achieves an optimal performance on the self-constructed dataset, with an mean average precision (mAP) of 81.2%, precision (P) of 82.8%, recall (R) of 78.3%, and F

Indexed as

missing seedlingsPCERT-DETRrice seedlingsUAV

Identifiers

PMID40733393

DOI10.3390/plants14142156

PMCPMC12298627

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