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 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.