@article{MB7C5D2CB,
title = "A Study on Meta-Learning Based Neural Architecture Search for Crop Disease Diagnosis in IoT Environments",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.9.1353",
author = "Ji-Uk Kim, Hyun-Suk Lee",
keywords = "Meta-Learning, Neural Architecture Search, Crop Disease Diagnosis, IoT",
abstract = "In agriculture, accurate and efficient diagnosis of crop diseases is essential to improve agricultural productivity and quality. However, in real-world agricultural environments, there are challenges such as the lack of high-quality data and the limited memory and computational capacity of IoT devices. Therefore, in order to address crop disease diagnosis with deep learning, a lightweight model with a small size should be able to effectively examine crop disease with a small amount of training data. In this paper, we propose a crop disease diagnosis method that applies a combination of meta learning and neural architecture search to address these issues. The proposed method searches for a meta-model that can be generalized for various crop disease diagnosis tasks with only a small amount of data, in a search space consisting of lightweight models. Through experiments on real-world crop disease dataset, we demonstrate that the model trained with the proposed method achieves an accuracy improvement of more than 15.5% with 98.7% fewer parameters compared to the model in the related work. These results show that the proposed method is feasible for crop disease diagnosis under the limited conditions of real-world agricultural environments."
}