Abstract: Convolutional neural network (CNN) and Transformers, which are widely used for Earhead counting, continue to struggle with capturing long-range dependencies efficiently and often exhibit false positives or false negatives in densely populated regions. To overcome these limitations, a novel counting approach named PCLMamba is proposed in this paper. This lightweight network is built on a Mamba-based architecture and incorporates segmentation guidance and frequency domain enhancement. A Grouping Directional Mamba Model (GDMM) is introduced, which processes image sequences in groups and performs parallel selective scanning in different orders, thereby efficiently capturing long-range dependencies with low computational cost. Local features and globally scanned features are integrated via a Channel Attention Fusion Module (CAFM). To reduce false positives and negatives, a segmentation module (Segmenter) is designed to supply prior knowledge, which suppresses background interference while highlighting foreground regions. Additionally, a Frequency Spatial Enhancement module(FSE) is proposed to reinforce intermediate features in both spatial and frequency domains, further boosting counting accuracy. Experimental evaluations on multiple plant counting datasets including MTC, WED, and SHC perform well. In particular, the Mean Absolute Error (MAE) of the three datasets above reaches 4.4, 4.2, and 15.4 respectively, and other performance is also better than the comparison method as a whole.

Key words: smart agriculture, earhead counting, State-Space Model (SSM), Fast Fourier Transform (FFT), frequency enhancement

摘要： 基于卷积神经网络(CNN)与Transformer的谷穗计数方法仍存在长距离建模困难、计算效率低以及密度误报与密度漏报的问题。为解决这些问题，提出名为PCLMamba的新计数方法。PCLMamba是一个基于分割引导与频域增强的轻量Mamba网络。该网络通过引入分组的多方向状态空间模型(GDMM)分组处理图像序列且并行地送入不同顺序的选择性扫描中，轻量且高效地捕获长距离关系。然后利用通道注意力融合模块(CAFM)融合局部特征与全局扫描特征。为了解决密度误报与密度漏报的问题，设计了分割模块(Segmenter)提供先验知识，以抑制背景和增强前景。同时设计了双域信息增强模块(FSE)，该模块将中间特征在空域与频域上进行处理增强，进一步提高计数精度。实验结果表明，PCLMamba在MTC、WED、SHC数据集的计数任务上表现出色，平均绝对误差(MAE)分别达到4.4、4.2、15.4，且其他指标也整体优于对比方法。

关键词: 智慧农业, 谷穗计数, 状态空间模型, 快速傅里叶变换, 频域增强