DigiCrop Farm Data: Precision Farming Data Analytics at TUM Farm

Overview

DigiCrop Farm Data is a collaborative project funded through the Hans Eisenmann-Forum für Agrarwissenschaften (HEF) at TUM, jointly led by Prof. Kang Yu (Precision Agriculture), Prof. Heinz Bernhardt (Agricultural Systems Engineering), and Prof. Timo Oksanen (Agricultural Robotics and Automation). The project develops precision farming data analytics workflows using multi-sensor UAV and field datasets collected at the TUM Dürnast research farm, bridging agronomy, sensing, robotics, and data science.

Objectives

• Integrate multi-sensor UAV data (multispectral, RGB, LiDAR) with ground-based farm records for comprehensive field-scale crop monitoring.
• Develop data analytics pipelines for extracting agronomically meaningful insights from heterogeneous precision farming datasets.
• Assess crop nitrogen status and NUE using drone remote sensing and field measurements at TUM’s experimental farm.
• Support farm management decisions by linking remote sensing outputs to fertilization and yield outcomes.

Methodology

The project combines:

• Cross-disciplinary collaboration between precision agriculture, agricultural engineering, agrimechatronics and robotics groups at TUM

• Digitize Field experiment data to enalbe reuse of historical farming data e.g., for enhancing variable nitrogen fertilization applications

• Data analytics and machine learning for trait retrieval, canopy characterization, and NUE assessment

• Mobile sensing and remote sensing data calibration at TUM’s Dürnast research farm for spatial crop monitoring

• Funded by: Hans Eisenmann-Forum für Agrarwissenschaften (HEF) Seed Fund 2022

Duration: 5/2022 – 5/2023

Related Publications

• Wang et al. (2025). Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat. Computers and Electronics in Agriculture. View