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Spatio-Temporal Graph Neural Networks for Late Blight Disease Forecasting

첫 페이지 보기
  • 발행기관
    한국AI디지털융합학회(구 한국디지털융합학회) 바로가기
  • 간행물
    IJICTDC 바로가기
  • 통권
    Vol 9 No 2 (2024.12)바로가기
  • 페이지
    pp.1-12
  • 저자
    Harish Chandra Bhandari, Roshan Subedi, Kumar Lama, Yagya Raj Pandeya, Rajendra Dhakal, Oshin Sharma, Rojina Shakya, Prajwal Thapa, Bauram Chaudhary
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A458892

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원문정보

초록

영어
Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

목차

Abstract
1. Introduction
2. Methodology
2.1. Data acquisition
2.2. Graph neural network
2.3. Mobile application
3. Result and discussion
3.1. Graph neural network performance
3.2. System deployment on mobile application
3.3. Validation of late blight
4. Conclusion
References

키워드

Late blight Graph neural network Mobile application Field validation Phytophthora infestans

저자

  • Harish Chandra Bhandari [ Department of Mathematics, Kathmandu University, Nepal/Artificial Intelligence and Smart System Research laboratory, Kathmandu University, Nepal ]
  • Roshan Subedi [ Department of Agriculture, Kathmandu University, Nepal ]
  • Kumar Lama [ Department of Agriculture, Kathmandu University, Nepal ]
  • Yagya Raj Pandeya [ Department of Artificial Intelligence, Kathmandu University, Nepal/ Guru Technology Pvt.Ltd., Kathmandu, Nepal/ Artificial Intelligence and Smart System Research laboratory, Kathmandu University, Nepal ] Corresponding Author
  • Rajendra Dhakal [ International Centre for Integrated Mountain Development, Kathmandu, Nepal ]
  • Oshin Sharma [ International Centre for Integrated Mountain Development, Kathmandu, Nepal ]
  • Rojina Shakya [ Department of Artificial Intelligence, Kathmandu University, Nepal ]
  • Prajwal Thapa [ Department of Artificial Intelligence, Kathmandu University, Nepal/Artificial Intelligence and Smart System Research laboratory, Kathmandu University, Nepal ]
  • Bauram Chaudhary [ Department of Artificial Intelligence, Kathmandu University, Nepal/Artificial Intelligence and Smart System Research laboratory, Kathmandu University, Nepal ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국AI디지털융합학회(구 한국디지털융합학회) [The Korean Academic Society of AI Digital Convergence]
  • 설립연도
    2015
  • 분야
    사회과학>경영학
  • 소개
    본 학회는 디지털 경영에 관련된 디지털 미디어, 디지털 통신, 디지털 방송, 디지털 콘텐츠, 디지털 문화, 디지털 사회, 디지털 유통, 디지털 금융, 디지털 물류, 디지털 정책, 디지털 기술, 디지털 교육 그리고 디지털과 아날로그의 비교 등에 대한 학제간 연구와 실사구시적인 적용을 통하여 디지털 경영의 발전과 한국이 세계적인 디지털 강국으로 성장하기 위한 학술적인 기반과 실무적인 지침을 조성하는 것을 목적으로 하고 있습니다.

간행물

  • 간행물명
    IJICTDC [International Journal of Information Communication Technology and Digital Convergence]
  • 간기
    반년간
  • pISSN
    2466-0094
  • 수록기간
    2016~2026
  • 십진분류
    KDC 300 DDC 303

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