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

arXiv: https://arxiv.org/abs/2411.03835 · HTML: https://mlsanity.com/paper/2411.03835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Challenges and Opportunities in Edge Computing](https://mlsanity.com/paper/1609.01967/) (2016)
- [OpenEI: An Open Framework for Edge Intelligence](https://mlsanity.com/paper/1906.01864/) (2019)
- [Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results](https://mlsanity.com/paper/2305.11990/) (2023)

## Built on by (most cited first, 1 of 1)

- [Edge-Based Standing-Water Detection via FSM-Guided Tiering and Multi-Model Consensus](https://mlsanity.com/paper/2604.03308/) (2026)

## Abstract (arXiv)

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.