Abstract
Responding to rising global food security needs, precision agriculture and deep learning-based plant disease diagnosis have become crucial. Yet, deploying high-precision models on edge devices is challenging. Most lightweight networks use attention mechanisms designed for generic object recognition, which poorly capture subtle pathological features like irregular lesion shapes and complex textures. To overcome this, we propose a twofold solution: first, using a training-free neural architecture search method (DeepMAD) to create an efficient network backbone for edge devices; second, introducing the Shape-Texture Attention Module (STAM). STAM splits attention into two branches -- one using deformable convolutions (DCNv4) for shape awareness and the other using a Gabor filter bank for texture awareness. On the public CCMT plant disease dataset, our STA-Net model (with 401K parameters and 51.1M FLOPs) reached 89.00% accuracy and an F1 score of 88.96%. Ablation studies confirm STAM significantly improves performance over baseline and standard attention models. Integrating domain knowledge via decoupled attention thus presents a promising path for edge-deployed precision agriculture AI. The source code is available at this https URL.
Abstract (translated)
为了应对全球粮食安全需求的增长，精准农业和基于深度学习的植物病害诊断变得至关重要。然而，在边缘设备上部署高精度模型面临挑战。大多数轻量级网络使用的是为通用物体识别设计的关注机制，这些机制难以捕捉到如不规则病变形状和复杂纹理等细微病理特征。为此，我们提出了一种两阶段解决方案：首先，采用无训练的神经架构搜索方法（DeepMAD）来创建一个适用于边缘设备的有效网络骨干；其次，引入形状-纹理注意力模块（STAM）。该模块将注意力分为两个分支——其中一个使用可变形卷积（DCNv4）以增强对形状的关注，另一个则利用Gabor滤波器组关注纹理。 在公开的CCMT植物病害数据集上，我们的STA-Net模型（具有401K参数和51.1M FLOPs）达到了89.00%的准确率以及88.96%的F1分数。消融研究表明，STAM显著提高了性能，在基准和标准注意力模型之上表现出优势。通过解耦注意力整合领域知识，为部署于边缘设备上的精准农业AI提供了有希望的发展路径。 源代码可在以下链接获取：[此URL]（请将括号内的文本替换为实际的网址）。
URL
https://arxiv.org/abs/2509.03754
