Work

An Edge Computing-Based Solution for Real-Time Leaf Disease Classification Using Thermal Imaging

Year: 2024

Type: article

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$1.48\times $faster on Edge TPU Max for VGG16, and up to$2.13\times $faster with precision reduction on Intel NCS2 for MobileNetV1, compared with high-end GPUs like RTX 3090, while maintaining state-of-the-art accuracy.

Source: IEEE Geoscience and Remote Sensing Letters

DOI: 10.1109/lgrs.2024.3456637

Authors: Públio Elon Correa da Silva, Jurandy Almeida

Institution: Universidade Federal de São Carlos

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: green