Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Deep Learning-Based Plant Health State Classification Using Image Data

영상 데이터를 이용한 딥러닝 기반 작물 건강 상태 분류 연구

  • Ali Asgher Syed (Department of Electronics Engineering, Jeonbuk National University) ;
  • Jaehawn Lee (Department of Electronics Engineering, Jeonbuk National University) ;
  • Alvaro Fuentes (Core Research Institute of Intelligent Robots, Jeonbuk National University) ;
  • Sook Yoon (Department of Computer Engineering, Mokpo National University) ;
  • Dong Sun Park (Core Research Institute of Intelligent Robots, Jeonbuk National University)
  • Received : 2024.06.28
  • Accepted : 2024.08.16
  • Published : 2024.08.31
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Abstract

Tomatoes are rich in nutrients like lycopene, β-carotene, and vitamin C. However, they often suffer from biological and environmental stressors, resulting in significant yield losses. Traditional manual plant health assessments are error-prone and inefficient for large-scale production. To address this need, we collected a comprehensive dataset covering the entire life span of tomato plants, annotated across 5 health states from 1 to 5. Our study introduces an Attention-Enhanced DS-ResNet architecture with Channel-wise attention and Grouped convolution, refined with new training techniques. Our model achieved an overall accuracy of 80.2% using 5-fold cross-validation, showcasing its robustness in precisely classifying the health states of tomato plants.

토마토에는 리코펜, β-카로틴 및 비타민 C와 같은 영양소가 풍부하고 세계적으로 많이 소비되는 채소 중 하나이다. 그러나 종종 생물학적 및 환경적 스트레스 요인으로 인해 수확량 손실이 발생한다. 전통적인 작물 건강 평가는 오류가 발생하기 쉽고 대규모 생산에 비효율적이다. 이러한 문제를 해결하기 위해 건강 상태에 대해 1~5로 주석을 메긴 토마토 전체 생육기간을 다루는 포괄적인 데이터 세트를 수집하였다. 우리는 Channel-wise attention과 Grouped convolution을 사용한 Attention-Enhanced DS-ResNet 아키텍처와 새로운 학습 기법을 제안한다. 우리의 모델은 5-fold 교차 검증을 사용하여 전체 정확도 80.2%를 달성하여 작물의 건강 상태를 정확하게 분류하는데 있어 견고성을 보여주었다.

Keywords

Acknowledgement

본 결과물은 농림축산식품부 및 과학기술정보통신부, 농촌진흥청의 재원으로 농림식품기술기획평가원과 재단법인 스마트팜연구개발사업단의 스마트팜다부처패키지혁신기술개발사업의 지원을 받아 연구되었음 (RS-2021-IP421005).

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