inis 21(26): e1

Research Article

An Efficient Pest Classification In Smart Agriculture Using Transfer Learning

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  • @ARTICLE{10.4108/eai.26-1-2021.168227,
        author={Tuan T. Nguyen and Quoc-Tuan Vien and Harin Sellahewa},
        title={An Efficient Pest Classification In Smart Agriculture Using Transfer Learning},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        keywords={Deep learning, Transfer Learning, Object Classification, Smart Agriculture},
  • Tuan T. Nguyen
    Quoc-Tuan Vien
    Harin Sellahewa
    Year: 2021
    An Efficient Pest Classification In Smart Agriculture Using Transfer Learning
    DOI: 10.4108/eai.26-1-2021.168227
Tuan T. Nguyen1,*, Quoc-Tuan Vien2, Harin Sellahewa1
  • 1: School of Computing, University of Buckingham, United Kingdom
  • 2: Faculty of Science and Technology, Middlesex University, United Kingdom
*Contact email:


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.