A Study on Meta-Learning Based Neural Architecture Search for Crop Disease Diagnosis in IoT Environments 


Vol. 50,  No. 9, pp. 1353-1363, Sep.  2025
10.7840/kics.2025.50.9.1353


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  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.

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[IEEE Style]

J. Kim and H. Lee, "A Study on Meta-Learning Based Neural Architecture Search for Crop Disease Diagnosis in IoT Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1353-1363, 2025. DOI: 10.7840/kics.2025.50.9.1353.

[ACM Style]

Ji-Uk Kim and Hyun-Suk Lee. 2025. A Study on Meta-Learning Based Neural Architecture Search for Crop Disease Diagnosis in IoT Environments. The Journal of Korean Institute of Communications and Information Sciences, 50, 9, (2025), 1353-1363. DOI: 10.7840/kics.2025.50.9.1353.

[KICS Style]

Ji-Uk Kim and Hyun-Suk Lee, "A Study on Meta-Learning Based Neural Architecture Search for Crop Disease Diagnosis in IoT Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1353-1363, 9. 2025. (https://doi.org/10.7840/kics.2025.50.9.1353)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880