用轻量模型实现边缘设备上的少样本植物病害识别。
Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices
- 基于病害感知的通道重要性评分，动态剪裁神经网络
- 模型缩小78%仍保持92.3%原始准确率，实时运行于树莓派
- 适合资源受限地区的小农户使用，支持少样本快速部署
偏远地区的农民需要快速可靠的植物病害识别方法，但往往缺乏实验室或高性能计算资源。深度学习模型虽能高精度识别叶部疾病，但通常过大且计算成本高，难以在树莓派等低成本边缘设备上运行。同时，收集数千张标注病害图像训练模型耗时且昂贵。本文通过结合神经网络剪枝（移除冗余结构）与少样本学习（从少量样本中学习），解决上述问题。提出病害感知通道重要性评分（DACIS），用于识别区分不同植物病害的关键网络部分，并集成到三阶段剪枝-元学习-再剪枝（PMP）流程中。在PlantVillage和PlantDoc数据集上的实验表明，该方法将模型大小减少78%，同时保留92.3%的原始准确率，压缩后模型在树莓派4上以7帧/秒速度运行，使小农户实现现场实时诊断成为可能。
原文摘要 · Abstract (English)
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.
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