An Edge Computing-Based Solution for Real-Time Leaf Disease Classification Using Thermal Imaging
P. Silva, Jurandy Almeida
Abstract
Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this letter, we explore the potential of edge computing (EC) for real-time classification of leaf diseases using thermal imaging. We present a thermal image dataset for plant disease classification and evaluate DL 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.48\times $ </tex-math></inline-formula> faster on Edge TPU Max for VGG16, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.13\times $ </tex-math></inline-formula> faster with precision reduction on Intel NCS2 for MobileNetV1, compared with high-end GPUs like RTX 3090, while maintaining state-of-the-art accuracy.