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.

In UAV (unmanned aerial vehicle)-enabled precision agriculture, achieving high-accuracy disease diagnosis and yield estimation simultaneously on resource-constrained edge devices remains a significant challenge. Existing solutions are commonly hindered by conflicts in visual feature scales, the absence of explicit agronomic causal logic, and the trade-off between lightweight design and global modeling capability. To address these challenges, a heterogeneous dual-stream state-space framework termed SymbioMamba is proposed. The proposed framework incorporates three key innovations: first, a heterogeneous dual-stream encoder is constructed, in which a micro-texture stream captures high-frequency disease details while a macro-context-scan stream models field-scale biomass continuity; second, a pathology–biomass collaborative interaction (PBCI) module is designed to explicitly inject the biological prior—disease stress leading to yield reduction—into the feature space. Third, a topology-aligning cross-architecture distillation (TACAD) paradigm is introduced to transfer global knowledge from a heavyweight teacher to a lightweight student. Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an $R^2$ of 0.915. Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines. Furthermore, yield prediction error is significantly reduced to an RMSE of 485.6 kg/ha. With a compact model size of 6.2 M parameters and 2.4 G FLOPs, SymbioMamba attains an inference speed of 38.2 FPS on the NVIDIA Jetson AGX Orin platform, providing a high-performance, real-time solution for intelligent agricultural phenotypic analysis.