The article introduces the updated version of agriGO (v2.0), a gene ontology (GO) analysis toolkit specifically designed for the agricultural community. The updated version includes significant enhancements such as an expanded number of supported species (394) and data types (865), improved computational efficiency, and new visualization features. The platform now supports batch analysis and provides P-value distribution (PVD) for better statistical analysis. Additionally, it offers enhanced user-friendliness with improved web page design and the addition of tools like SEACOMPARE for cross-comparison, direct acyclic graph (DAG) and Scatter Plots for visualizing significant GO terms. The updated agriGO v2.0 is publicly accessible at http://systemsbiology.cau.edu.cn/agriGOv2/. The article also discusses the methods used for statistical testing, multiple hypothesis adjustment, and the processing flow for predicting GO annotations, emphasizing the importance of reliable GO annotations and the integration of various sources to enhance the quality and coverage of GO data.The article introduces the updated version of agriGO (v2.0), a gene ontology (GO) analysis toolkit specifically designed for the agricultural community. The updated version includes significant enhancements such as an expanded number of supported species (394) and data types (865), improved computational efficiency, and new visualization features. The platform now supports batch analysis and provides P-value distribution (PVD) for better statistical analysis. Additionally, it offers enhanced user-friendliness with improved web page design and the addition of tools like SEACOMPARE for cross-comparison, direct acyclic graph (DAG) and Scatter Plots for visualizing significant GO terms. The updated agriGO v2.0 is publicly accessible at http://systemsbiology.cau.edu.cn/agriGOv2/. The article also discusses the methods used for statistical testing, multiple hypothesis adjustment, and the processing flow for predicting GO annotations, emphasizing the importance of reliable GO annotations and the integration of various sources to enhance the quality and coverage of GO data.