Multi-view hypergraph networks incorporating interpretability analysis for predicting lodging in corn varieties

Abstract

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.

Plant phenotyping relevance

トウモロコシの倒伏という植物状態を予測する解釈可能なマルチビュー・ハイパーグラフ手法が研究の中心であり、表現型データを用いた実データ評価も行っているため、植物フェノタイピング手法として含める。

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