This project aims to discover the knowledge and practices to improve soil health, soybean seed quality and production using digital agriculture technologies. We proposed and tested a method to quickly quantify soil health using drone imagery and soil sensor data that are analyzed using machine learning. The project is expected to result in a tool to acquire high-resolution soil health map in a scaled field. We hope to identify improved management practices to improve soil health, soybean seed nutrition quality and sustainability. The proposed key deliverables include: (1) a fast soil health mapping tool using drone imaging and machine learning technology, (2) a validated correlation between soil health and management practices of soil and crop, and (3) Knowledge about the correlation of soil health and soybean seed quality. The milestones of the project include: (1) Development of an optimized data acquisition system to collect field data of soil, crop and climate; (2) establishment of a training data set for modeling in years of 2022 and 2023 soybean seasons; (3) development of a data analysis and modeling pipeline; and (4) Reach out stakeholders through field days and presentations.