Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural network pruning -- removing unnecessary parts of the model -- with few-shot learning, which enables the model to learn from limited examples. This paper proposes Disease-Aware Channel Importance Scoring (DACIS), a method that identifies which parts of the neural network are most important for distinguishing between different plant diseases, integrated into 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, with the compressed model running at 7 frames per second on a Raspberry Pi 4, making real-time field diagnosis practical for smallholder farmers.

翻译：偏远地区的农民需要快速可靠的植物病害识别方法，但他们往往无法使用实验室或高性能计算资源。深度学习模型能够通过叶片图像高精度地检测病害，但这些模型通常过于庞大且计算成本高昂，难以在树莓派等低成本边缘设备上运行。此外，收集数千张带标签的病害图像用于训练既昂贵又耗时。本文通过结合神经网络剪枝（移除模型中不必要的部分）与少样本学习（使模型能够从有限样本中学习），同时应对这两个挑战。本文提出疾病感知通道重要性评分（DACIS）方法，该方法能识别神经网络中哪些部分对于区分不同植物病害最为关键，并将其集成到三阶段“剪枝-元学习-再剪枝”（PMP）流程中。在PlantVillage和PlantDoc数据集上的实验表明，所提方法在保持原始精度92.3%的同时，将模型尺寸缩减了78%，压缩后的模型在树莓派4上能够以每秒7帧的速度运行，为小农户实现实时田间病害诊断提供了可行方案。