https://paglab.org/tag/tum-digifarm/TUM DigiFarmHugo Blox Builder (https://hugoblox.com)en-usSun, 01 May 2022 00:00:00 +0000https://paglab.org/media/logo_hu_32055a858e223df5.pnghttps://paglab.org/tag/tum-digifarm/https://paglab.org/project/digicrop-farm-data/Sun, 01 May 2022 00:00:00 +0000https://paglab.org/project/digicrop-farm-data/<h2 id="overview">Overview</h2> <p><strong>DigiCrop Farm Data</strong> is a collaborative project funded through the <strong>Hans Eisenmann-Forum für Agrarwissenschaften (HEF)</strong> at TUM, jointly led by <strong>Prof. Kang Yu</strong> (Precision Agriculture), <strong>Prof. Heinz Bernhardt</strong> (Agricultural Systems Engineering), and <strong>Prof. Timo Oksanen</strong> (Agricultural Robotics and Automation). The project develops <strong>precision farming data analytics</strong> workflows using multi-sensor UAV and field datasets collected at the <strong>TUM Dürnast research farm</strong>, bridging agronomy, sensing, robotics, and data science.</p> <h2 id="objectives">Objectives</h2> <ul> <li><strong>Integrate multi-sensor UAV data</strong> (multispectral, RGB, LiDAR) with ground-based farm records for comprehensive field-scale crop monitoring.</li> <li><strong>Develop data analytics pipelines</strong> for extracting agronomically meaningful insights from heterogeneous precision farming datasets.</li> <li><strong>Assess crop nitrogen status and NUE</strong> using drone remote sensing and field measurements at TUM&rsquo;s experimental farm.</li> <li><strong>Support farm management decisions</strong> by linking remote sensing outputs to fertilization and yield outcomes.</li> </ul> <h2 id="methodology">Methodology</h2> <p>The project combines:</p> <ul> <li> <p><strong>Cross-disciplinary collaboration</strong> between precision agriculture, agricultural engineering, agrimechatronics and robotics groups at TUM</p> </li> <li> <p><strong>Digitize Field experiment data</strong> to enalbe reuse of historical farming data e.g., for enhancing variable nitrogen fertilization applications</p> </li> <li> <p><strong>Data analytics and machine learning</strong> for trait retrieval, canopy characterization, and NUE assessment</p> </li> <li> <p><strong>Mobile sensing and remote sensing data calibration</strong> at TUM&rsquo;s Dürnast research farm for spatial crop monitoring</p> </li> <li> <p><strong>Funded by:</strong> Hans Eisenmann-Forum für Agrarwissenschaften (HEF) Seed Fund 2022</p> </li> </ul> <p><strong>Duration:</strong> 5/2022 – 5/2023</p> <h2 id="related-publications">Related Publications</h2> <ul> <li>Wang et al. (2025). <em>Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat.</em> <strong>Computers and Electronics in Agriculture</strong>. <a href="../../publication/wang-2025-cea-drone/">View</a></li> </ul>