UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/14/2156 |
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| author | Jiaxin Gao Feng Tan Zhaolong Hou Xiaohui Li Ailin Feng Jiaxin Li Feiyu Bi |
| author_facet | Jiaxin Gao Feng Tan Zhaolong Hou Xiaohui Li Ailin Feng Jiaxin Li Feiyu Bi |
| author_sort | Jiaxin Gao |
| collection | DOAJ |
| description | 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<sub>1</sub>-score (F<sub>1</sub>) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F<sub>1</sub>, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices. |
| format | Article |
| id | doaj-art-1a8b2dd2fc714b5b8b1a558896a77765 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-1a8b2dd2fc714b5b8b1a558896a777652025-08-20T03:32:27ZengMDPI AGPlants2223-77472025-07-011414215610.3390/plants14142156UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR ModelJiaxin Gao0Feng Tan1Zhaolong Hou2Xiaohui Li3Ailin Feng4Jiaxin Li5Feiyu Bi6College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaDue 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<sub>1</sub>-score (F<sub>1</sub>) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F<sub>1</sub>, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices.https://www.mdpi.com/2223-7747/14/14/2156PCERT-DETRUAVrice seedlingsmissing seedlings |
| spellingShingle | Jiaxin Gao Feng Tan Zhaolong Hou Xiaohui Li Ailin Feng Jiaxin Li Feiyu Bi UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model Plants PCERT-DETR UAV rice seedlings missing seedlings |
| title | UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model |
| title_full | UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model |
| title_fullStr | UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model |
| title_full_unstemmed | UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model |
| title_short | UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model |
| title_sort | uav based automatic detection of missing rice seedlings using the pcert detr model |
| topic | PCERT-DETR UAV rice seedlings missing seedlings |
| url | https://www.mdpi.com/2223-7747/14/14/2156 |
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