A Transformer-Based Symmetric Diffusion Segmentation Network for Wheat Growth Monitoring and Yield Counting

Ziyang Jin, Wenjie Hong, Yuru Wang, Chenlu Jiang, Boming Zhang, Zhengxi Sun, Shijie Liu and Chunli Lv ()
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Ziyang Jin: China Agricultural University, Beijing 100083, China
Wenjie Hong: China Agricultural University, Beijing 100083, China
Yuru Wang: China Agricultural University, Beijing 100083, China
Chenlu Jiang: China Agricultural University, Beijing 100083, China
Boming Zhang: China Agricultural University, Beijing 100083, China
Zhengxi Sun: China Agricultural University, Beijing 100083, China
Shijie Liu: China Agricultural University, Beijing 100083, China
Chunli Lv: China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 7, 1-26

Abstract: 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.

Keywords: wheat growth monitoring; precision agriculture; yield prediction; instance segmentation; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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