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Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms

Author

Listed:
  • Haram Kim

    (Department of IT Distribution and Logistics, Soongsil University, Seoul 06978, Republic of Korea)

  • Dongsoo Kim

    (Department of Industrial and Information Systems Engineering, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

This paper presents a deep-learning-based classification model that aims to detect diverse pest infections in strawberry plants. The proposed model enables the timely identification of pest symptoms, allowing for prompt and effective pest management in smart farms. The present research employed an actual dataset of strawberry leaf images collected from a smart farm test bed. To expand the dataset, open data from sources such as Kaggle were utilized, while diseased leaf images were obtained through web crawling with the aid of the Python library. Subsequently, the expanded and added data were resized to a uniform size, and Pseudo-Labeling was implemented to ensure stable learning for both the training and test datasets. The RegNet and EfficientNet models were selected as the primary CNN-based image network models for repetitive learning, and ensemble learning was employed to enhance prediction accuracy. The proposed model is anticipated to facilitate the early identification and treatment of pests on strawberry leaves during the seedling period, a pivotal phase in smart farm development. Furthermore, it is expected to boost production in the agricultural industry and strengthen its competitive edge.

Suggested Citation

  • Haram Kim & Dongsoo Kim, 2023. "Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7931-:d:1145343
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    Citations

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    Cited by:

    1. Jianping Wang & Zhiyu Li & Guohong Gao & Yan Wang & Chenping Zhao & Haofan Bai & Yingying Lv & Xueyan Zhang & Qian Li, 2024. "BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification," Agriculture, MDPI, vol. 14(5), pages 1-25, April.
    2. Aurel Mihail Țîțu & Vasile Gusan & Mihai Dragomir & Alina Bianca Pop & Ștefan Țîțu, 2024. "Cost Calculation and Deployment Strategies for Collaborative Robots in Production Lines: An Innovative and Sustainable Perspective in Knowledge-Based Organizations," Sustainability, MDPI, vol. 16(13), pages 1-25, June.

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