An efficient pest classification in smart agriculture using transfer learning

Nguyen, Tuan T., Vien, Quoc-Tuan ORCID logoORCID: https://orcid.org/0000-0001-5490-904X and Sellahewa, Harin (2021) An efficient pest classification in smart agriculture using transfer learning. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 8 (26) , e1. pp. 1-8. ISSN 2410-0218 [Article] (doi:10.4108/eai.26-1-2021.168227)

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Abstract

To this day, agriculture still remains very important and plays considerable role to support our daily life and economy in most countries. It is the source of not only food supply, but also providing raw materials for other industries, e.g. plastic, fuel. Currently, farmers are facing the challenge to produce sufficient crops for expanding human population and growing in economy, while maintaining the quality of agriculture products. Pest invasions, however, are a big threat to the growth crops which cause the crop loss and economic consequences. If they are left untreated even in a small area, they can quickly spread out other healthy area or nearby countries. A pest control is therefore crucial to reduce the crop loss. In this paper, we introduce an efficient method basing on deep learning approach to classify pests from images captured from the crops. The proposed method is implemented on various EfficientNet and shown to achieve a considerably high accuracy in a complex dataset, but only a few iterations are required in the training process.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 31859
Notes on copyright: Copyright © 2021 Tuan T. Nguyen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
Useful Links:
Depositing User: Quoc-Tuan Vien
Date Deposited: 20 Jan 2021 14:12
Last Modified: 07 Sep 2021 00:04
URI: https://eprints.mdx.ac.uk/id/eprint/31859

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