A key challenge in agricultural AI is deploying disease detection systems in remote fields with limited access to laboratories or high-performance computing (HPC) resources. While deep learning (DL) models, specifically deep convolutional networks, achieve high accuracy in identifying plant pathologies from leaf imagery, their memory footprints and computational demands limit edge deployment on devices constrained by battery life, processing power, and connectivity, such as Raspberry Pi. Few-shot learning (FSL) paradigms offer a compelling solution to the data scarcity problem inherent in agricultural applications, where obtaining labeled samples for novel disease variants proves both costly and time-sensitive. This work introduces a framework combining pruning with meta-learning for agricultural disease classification, addressing the tension between generalization capability and deployment feasibility. The proposed approach combines a novel Disease-Aware Channel Importance Scoring (DACIS) mechanism with a three-stage Prune-then-Meta-Learn-then-Prune (PMP) pipeline. Experiments on PlantVillage and PlantDoc datasets demonstrate that the proposed approach reduces model size by 78\% while maintaining 92.3\% of the original accuracy. The compressed model achieves 7 frames per second (FPS) on a Raspberry Pi 4, enabling practical real-time field diagnosis for smallholder farmers.

翻译：农业人工智能面临的一个关键挑战是在偏远农田部署病害检测系统，这些地区通常难以获得实验室或高性能计算资源。尽管深度学习模型，特别是深度卷积网络，在基于叶片图像的植物病理识别中实现了高精度，但其内存占用和计算需求限制了在边缘设备上的部署，例如树莓派等受电池寿命、处理能力和连接性约束的设备。少样本学习范式为农业应用中固有的数据稀缺问题提供了有效的解决方案，因为在农业领域获取新型病害变种的标记样本既成本高昂又具有时间敏感性。本研究提出了一种结合剪枝与元学习的农业病害分类框架，以解决泛化能力与部署可行性之间的矛盾。所提出的方法将新颖的病害感知通道重要性评分机制与三阶段“剪枝-元学习-剪枝”流程相结合。在PlantVillage和PlantDoc数据集上的实验表明，该方法在保持原始精度92.3%的同时，将模型大小减少了78%。压缩后的模型在树莓派4上达到每秒7帧的处理速度，为小农户实现了实用的实时田间诊断。