The　timely　and　accurate　prediction　of　maize　(Zea　mays　L.)　yields　prior　to　harvest　is　critical　for　food　security　and　agricultural　policy　development.　Currently,　many　researchers　are　using　machine　learning　and　deep　learning　to　predict　maize　yields　in　specific　regions　with　high　accuracy.　However,　existing　methods　typically　have　two　limitations.　One　is　that　they　ignore　the　extensive　correlation　in　maize　planting　data,　such　as　the　association　of　maize　yields　between　adjacent　planting　locations　and　the　combined　effect　of　meteorological　features　and　maize　traits　on　maize　yields.　The　other　issue　is　that　the　performance　of　existing　models　may　suffer　significantly　when　some　data　in　maize　planting　records　is　missing,　or　the　samples　are　unbalanced.　Therefore,　this　paper　proposes　an　end-to-end　bipartite　graph　neural　network-based　model　for　trait　data　imputation　and　yield　prediction.　The　maize　planting　data　is　initially　converted　to　a　bipartite　graph　data　structure.　Then,　a　yield　prediction　model　based　on　a　bipartite　graph　neural　network　is　developed　to　impute　missing　trait　data　and　predict　maize　yield.　This　model　can　mine　correlations　between　different　samples　of　data,　correlations　between　different　meteorological　features　and　traits,　and　correlations　between　different　traits.　Finally,　to　address　the　issue　of　unbalanced　sample　size　at　each　planting　location,　we　propose　a　loss　function　based　on　the　gradient　balancing　mechanism　that　effectively　reduces　the　impact　of　data　imbalance　on　the　prediction　model.　When　compared　to　other　data　imputation　and　prediction　models,　our　method　achieves　the　best　yield　prediction　result　even　when　missing　data　is　not　pre-processed.