A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting
A wheat growth and counting analysis model based on instance segmentation is proposed in this study to address the challenges of wheat growth monitoring and yield prediction in high-density agricultural environments. The model integrates the transformer architecture with a symmetric attention mechan...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/7/670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850212571220541440 |
|---|---|
| author | Ziyang Jin Wenjie Hong Yuru Wang Chenlu Jiang Boming Zhang Zhengxi Sun Shijie Liu Chunli Lv |
| author_facet | Ziyang Jin Wenjie Hong Yuru Wang Chenlu Jiang Boming Zhang Zhengxi Sun Shijie Liu Chunli Lv |
| author_sort | Ziyang Jin |
| collection | DOAJ |
| description | A wheat growth and counting analysis model based on instance segmentation is proposed in this study to address the challenges of wheat growth monitoring and yield prediction in high-density agricultural environments. The model integrates the transformer architecture with a symmetric attention mechanism and employs a symmetric diffusion module for precise segmentation and growth measurement of wheat instances. By introducing an aggregated loss function, the model effectively optimizes both segmentation accuracy and growth measurement performance. Experimental results show that the proposed model excels across several evaluation metrics. Specifically, in the segmentation accuracy task, the wheat instance segmentation model using the symmetric attention mechanism achieved a Precision of 0.91, Recall of 0.87, Accuracy of 0.89, mAP@75 of 0.88, and F1-score of 0.89, significantly outperforming other baseline methods. For the growth measurement task, the model’s Precision reached 0.95, Recall was 0.90, Accuracy was 0.93, mAP@75 was 0.92, and F1-score was 0.92, demonstrating a marked advantage in wheat growth monitoring. Finally, this study provides a novel and effective method for precise growth monitoring and yield counting in high-density agricultural environments, offering substantial support for future intelligent agricultural decision-making systems. |
| format | Article |
| id | doaj-art-d2a1531c9bc643ff9d37cd09d1aa4acb |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d2a1531c9bc643ff9d37cd09d1aa4acb2025-08-20T02:09:18ZengMDPI AGAgriculture2077-04722025-03-0115767010.3390/agriculture15070670A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield CountingZiyang Jin0Wenjie Hong1Yuru Wang2Chenlu Jiang3Boming Zhang4Zhengxi Sun5Shijie Liu6Chunli Lv7China Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaA wheat growth and counting analysis model based on instance segmentation is proposed in this study to address the challenges of wheat growth monitoring and yield prediction in high-density agricultural environments. The model integrates the transformer architecture with a symmetric attention mechanism and employs a symmetric diffusion module for precise segmentation and growth measurement of wheat instances. By introducing an aggregated loss function, the model effectively optimizes both segmentation accuracy and growth measurement performance. Experimental results show that the proposed model excels across several evaluation metrics. Specifically, in the segmentation accuracy task, the wheat instance segmentation model using the symmetric attention mechanism achieved a Precision of 0.91, Recall of 0.87, Accuracy of 0.89, mAP@75 of 0.88, and F1-score of 0.89, significantly outperforming other baseline methods. For the growth measurement task, the model’s Precision reached 0.95, Recall was 0.90, Accuracy was 0.93, mAP@75 was 0.92, and F1-score was 0.92, demonstrating a marked advantage in wheat growth monitoring. Finally, this study provides a novel and effective method for precise growth monitoring and yield counting in high-density agricultural environments, offering substantial support for future intelligent agricultural decision-making systems.https://www.mdpi.com/2077-0472/15/7/670wheat growth monitoringprecision agricultureyield predictioninstance segmentationdeep learning |
| spellingShingle | Ziyang Jin Wenjie Hong Yuru Wang Chenlu Jiang Boming Zhang Zhengxi Sun Shijie Liu Chunli Lv A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting Agriculture wheat growth monitoring precision agriculture yield prediction instance segmentation deep learning |
| title | A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting |
| title_full | A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting |
| title_fullStr | A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting |
| title_full_unstemmed | A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting |
| title_short | A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting |
| title_sort | transformer based symmetric diffusion segmentation network for wheat growth monitoring and yield counting |
| topic | wheat growth monitoring precision agriculture yield prediction instance segmentation deep learning |
| url | https://www.mdpi.com/2077-0472/15/7/670 |
| work_keys_str_mv | AT ziyangjin atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT wenjiehong atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT yuruwang atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT chenlujiang atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT bomingzhang atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT zhengxisun atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT shijieliu atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT chunlilv atransformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT ziyangjin transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT wenjiehong transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT yuruwang transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT chenlujiang transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT bomingzhang transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT zhengxisun transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT shijieliu transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting AT chunlilv transformerbasedsymmetricdiffusionsegmentationnetworkforwheatgrowthmonitoringandyieldcounting |