Sebastian Anderson Guerrero1, Diego Fernando Angrino Chiran1, Oscar Javier Munar Vivas1 and Alexander Mauricio Caballero Rodríguez2
1Colombian Sugarcane Research Center (Cenicaña), Florida, Valle del Cauca, Colombia
2Risaralda Sugar Mill, La Virginia, Risaralda, Colombia
Identifying and correcting deformed furrows are critical for optimizing water management and machinery performance in sugarcane cultivation, particularly in regions prone to flooding, such as the Cauca River Valley. Deformed furrows disrupt drainage, creating flood-prone areas that lead to crop losses and operational challenges, including potential damage to the basecutter of harvesters. This study introduces a LiDAR-based approach using the DJI Matrice 300 RTK drone to quantify furrow deformations and implement soil management improvements. Furrow lines generated by tractors equipped with RTK technology were buffered to create polygons representing furrow dimensions, which were segmented into 5-m sections for elevation analysis. Points with elevations more than 20 cm below the average height of each segment were classified as deformations. The study calculated the percentage of deformed furrows in the field and identified flood-prone areas. This methodology demonstrated its effectiveness in rapidly and accurately identifying areas of concern, enabling precise interventions in soil management. The results underscore the operational, economic, and environmental benefits of leveraging LiDAR technology to improve agricultural practices. The findings emphasize the importance of preventing damage to harvesting machinery by addressing furrow deformations, further enhancing farm efficiency and sustainability.