Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this paper, we explore the potential of edge computing for real-time classification of leaf diseases using thermal imaging. We present a thermal image dataset for plant disease classification and evaluate deep learning models, including InceptionV3, MobileNetV1, MobileNetV2, and VGG-16, on resource-constrained devices like the Raspberry Pi 4B. Using pruning and quantization-aware training, these models achieve inference times up to 1.48x faster on Edge TPU Max for VGG16, and up to 2.13x faster with precision reduction on Intel NCS2 for MobileNetV1, compared to high-end GPUs like the RTX 3090, while maintaining state-of-the-art accuracy.

翻译：深度学习技术能够通过改善作物健康监测与管理来变革农业，从而提升食品安全。本文探讨了利用边缘计算实现基于热成像的叶片病害实时分类的潜力。我们提出了一个用于植物病害分类的热成像数据集，并在树莓派4B等资源受限设备上评估了包括InceptionV3、MobileNetV1、MobileNetV2和VGG-16在内的深度学习模型。通过剪枝和量化感知训练，这些模型在Edge TPU Max上实现了最高达1.48倍（VGG16）的推理加速，在Intel NCS2上通过精度缩减实现了最高达2.13倍（MobileNetV1）的加速，相较于RTX 3090等高端GPU，同时保持了最先进的分类精度。