인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In recent years, the accurately and timely predict the crop yield has become hard to achieve because crop growth has complex and changing patterns across space and time. However, the existing model struggle to capture dynamic spatial temporal, nonlinear and external environmental factors such as temperature, soil conditions and precipitation, due to its graph performance. To overcome this problem, this paper proposed a Knowledge guided Graph Attention network with a Mixture Density Network (KGAT-MDN) to predict crop yield with more flexibility. The proposed KGAT-MDN first uses a 3D Convolutional Neural Network (CNN) to extracts important features. Next, the graph attention network used to model, the proposed method as a node-level regression problem on a graph built from the distribution system. After, mixture density networks are used to predict multiple possible outcomes at once, along with their importance, which are represented as Gaussian distributions. At last, location-aware Spatial Attention Graph Network is used, which uses geospatial knowledge to combine the features of nearby areas used for the final result. The experiments results obtained 0.50 for RMSE, 0.95 for R 2 and 10.11 for MAPE on Crop yield prediction dataset by outperforming the existing KSTAGE in predicting crop yield in agriculture.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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