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

Jiaxin Gao¹, Feng Tan², Zhaolong Hou¹

¹College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Plants (Basel, Switzerland)

|July 30, 2025

Summary

This study introduces an improved PCERT-DETR model for detecting missing rice seedlings using drone imagery. The method enhances accuracy and efficiency for precision farming, aiding reseeding efforts.

Keywords:

PCERT-DETR UAV missing seedlings rice seedlings

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Area of Science:

Agricultural Engineering
Remote Sensing
Computer Vision

Background:

Sowing machine limitations and low germination rates cause missing rice seedlings, impacting yield.
Existing unmanned aerial vehicle (UAV) methods for detecting missing seedlings in large fields are often ineffective.
Accurate detection of missing seedlings is crucial for timely reseeding and optimizing rice production.

Purpose of the Study:

To develop a fast and accurate UAV remote-sensing-based method for detecting missing rice seedlings in large fields.
To improve the efficiency and effectiveness of reseeding operations through precise seedling gap identification.
To contribute to the advancement of precision farming practices in rice cultivation.

Main Methods:

An improved PCERT-DETR model was utilized for detecting rice seedlings and identifying missing ones in UAV remote sensing images.
The model was trained and evaluated on a self-constructed dataset of large-field rice images.
Performance was assessed using metrics such as mean average precision (mAP), precision (P), recall (R), and F1-score (F1), alongside parameter count and FLOPs for efficiency.

Main Results:

The PCERT-DETR model achieved optimal performance with an mAP of 81.2%, P of 82.8%, R of 78.3%, and F1 of 80.5% on the self-constructed dataset.
The model demonstrated real-time detection capabilities with a parameter count of 21.4 M and 66.6 G FLOPs.
PCERT-DETR significantly outperformed baseline network models, improving P, R, F1, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively.

Conclusions:

The proposed PCERT-DETR model accurately detects missing rice seedlings in large fields using UAV remote sensing.
The method provides essential data for reseeding operations, supporting precision agriculture.
This research offers a robust solution for identifying seedling gaps, ultimately contributing to increased rice yields and farming efficiency.