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Development of AI-based Smart Agriculture Early Warning System

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(12), pp.67-77
  • DOI : 10.9708/jksci.2023.28.12.067
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 21, 2023
  • Accepted : December 11, 2023
  • Published : December 29, 2023

Sim, Hyun 1 Hyunwook Kim 2

1순천대학교
2코너스톤

Accredited

ABSTRACT

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.