Multi-view hypergraph networks incorporating interpretability analysis for predicting lodging in corn varieties
文献类型： 外文期刊
作者： Wang, Kaiyi 1 ; Yang, Feng 1 ; Bai, Wenqin 2 ; Wang, Shun 3 ; Zhang, Yong 3 ; Zhao, Xiangyu 1 ; Liu, Zhongqiang 1 ; Zhang, Qiusi 1 ; Li, Jinlong 1 ; Zhang, Dongfeng 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
2.Chongqing Acad Agr Sci, Chongqing 401329, Peoples R China
3.Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
4.Natl Innovat Ctr Digital Seed Ind, Beijing 100097, Peoples R China
关键词： Corn lodging classification; Multi-view hypergraph network; Graph interpretability
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）
ISSN： 0168-1699
年卷期： 2025 年 233 卷
页码：
收录情况： SCI
摘要： Accurately predicting the degree of lodging (breaking or bending of plants due to loss of integrity of the stem or roots) in corn (Zea mays) facilitates variety selection for the seed industry and growers, and provides data to support agricultural insurance claims. Due to various factors such as genetic characteristics and environment, accurately predicting lodging in corn varieties faces challenges in terms of data and models. In this study, we introduce an innovative classification model incorporated high-order relationships with multiple hidden factors to predict the degree of lodging in corn cultivation. Our model integrates a multi-view hypergraph network and incorporates an interpretability analysis component to enhance its predictive capabilities. To effectively capture the complex interactions within corn variety test samples across various dimensions, our model constructs a multi-view hypergraph using meteorological, disease infestation, and phenotype data. This method enables the model to comprehensively identify potential correlations in the test sample data of corn varieties, while considering the multidimensional nature of the problem. Furthermore, to enhance the model's interpretability, we employ an analytical method to quantify the influence of individual factors on the likelihood and severity of corn lodging events. These insights are then used to fine-tune the model's predictions. As an empirical evaluation, we applied this model to data collected from 194 corn test sites across mainland China. The results underscore the model's exceptional performance in predicting the degree of corn lodging, and demonstrate the effectiveness of potential correlation relationships in improving prediction accuracy.
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