In precision agriculture, where data is used to automate and optimize agricultural tasks for maximum yield, large-scale mapping and digital modeling of the land of interest are essential. This digital backbone enables tasks, such as yield estimation, targeted spraying, and phenotyping, while also providing the foundation for autonomous navigation, perception, and robotic operation. However, in modern commercial orchards, with their large scales, high tree density, and structured layouts, achieving consistent mapping and accurate 3D modeling remains challenging.
Drone-based remote-sensing imagery (RSI), aided by global navigation satellite systems (GNSS), can generate accurate aerial maps. However, aerial RSI cannot reliably capture important details beneath dense tree canopies. On the other hand, ground-based robots using LiDAR-inertial odometry can produce high-fidelity 3D point clouds that capture both individual tree structures and orchard row layouts. Despite these advantages, weakened satellite signals under dense foliage cause their estimated trajectories to accumulate drift over long distances, reducing localization accuracy.
Previous studies have explored multi-sensor fusion techniques that utilize aerial maps to calibrate trajectories for ground robots and suppress long-term drift. Conventional matching techniques, however, are highly sensitive to differences between aerial imagery and LiDAR data. Their accuracy is further affected by seasonal appearance changes, inconsistent textures, and repetitive canopy patterns, limiting global consistency for practical applications.
To address these limitations, a research team led by Professor Kyeong-Hwan Lee from the Department of Convergence Biosystems Engineering at Chonnam National University in South Korea developed a novel cross-modal fusion framework, integrating low-altitude drone RSI with ground robot LiDAR-inertial measurement unit (IMU) odometry (LIO). “Our system utilizes deep learning-based cross-modal alignment to connect these two sources of information, identifying the same tree rows, canopy patterns, and open spaces in aerial images and in the robot’s laser measurements, ” explains Prof. Lee. “ This way, the aerial map can be used as a reliable geographic reference, placing the ground robot’s detailed measurements accurately within the larger orchard map. ” Their study was made available online on March 26, 2026, and published in Volume 16, Issue 2 of the Artificial Intelligence in Agriculture journal on June 01, 2026.
The team first acquired RSI data using a drone platform, capturing high-resolution aerial images of an apple orchard in a pre-planned flight pattern. These images were processed to create a structured RSI-tiled database, and from this an RSI local-map was created for a small area within the orchard. In the same orchard, a ground robot equipped with a LiDAR-IMU system collected LiDAR point cloud data. This was then processed to create a structured 2D Bird’s-Eye View (BEV) local-map for the same area as the RSI local-map, encoding essential 3D structural information, including canopy height variations, surface reflectance, and local structural density.
To align the two data sources, the 2D BEV was then matched with the corresponding RSI local-map using a cross-view LIO-RSI fusion matching network. This deep learning model, featuring dual-branch feature extraction, a transformer-based cross-attention module, and a multi-scale flow refinement module, utilized pixel-level structural cues to reconstruct and accurately align the LiDAR map with the aerial map, substantially reducing accumulated drift.
The aligned data were then incorporated into a pose-graph estimation framework to create a geographic information system-driven multi-layer orchard model. This digital model can be used to extract phenotypic traits, including tree height, as well as information about tree health.
In tests covering approximately 1.3 kilometers of orchard travel, the fusion framework achieved localization accuracy on the order of a few centimeters, demonstrated robustness to seasonal variations, and suppressed long-term drift more effectively than conventional approaches. Moreover, the system is suitable for deployment on embedded devices for real-time operation.
“ By integrating what a robot sees on the ground with an aerial map, our system can help agricultural robots work reliably in orchards, supporting practical tasks such as crop inspection, targeted spraying, mowing, transportation, and harvesting, ” concludes Prof. Lee. “ In the future, such information could lead to living digital models of farms and orchards, helping them adapt to seasons and environmental and societal pressures. Ultimately, this will help farmers produce food more efficiently and sustainably. ”
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Reference
DOI: 10.1016/j.aiia.2026.03.005
About the Institute
Chonnam National University (CNU), established in 1952, is one of South Korea's leading national universities located in Gwangju. Building on its founding commitment to cultivating leaders of integrity and professional excellence, CNU contributes to national development and global progress through the pursuit of knowledge, ethical responsibility, and inclusive excellence. Guided by the core motto “Truth, Creativity, and Service,” the university advances research, education, and public engagement that strengthen resilient societies, foster sustainable development, and promote the well-being of future generations. As a trusted partner in the global community, CNU remains dedicated to addressing complex challenges in an increasingly interconnected world.
Website: https://global.jnu.ac.kr/jnumain_en.aspx
About Professor Kyeong-Hwan Lee
Dr. Kyeong-Hwan Lee is a Professor at Chonnam National University, South Korea, and serves as Director of both the AI AgriTech Research Center and the Sensors and Intelligent Biosystems Laboratory (SIBL). He received his Ph.D. in Biological and Agricultural Engineering from Kansas State University. His research focuses on robotics, computer vision, artificial intelligence, and digital technologies for next-generation agriculture systems. His team’s work encompasses 3D reconstruction of agricultural environments, 3D phenotyping, digital agriculture, and fully automated digital farming systems.
Artificial Intelligence in Agriculture
Experimental study
Not applicable
Transformer-based cross-view LiDAR–orthomosaic fusion for geo-localization and digital modeling in apple orchards
1-Jun-2026
The authors declare that they have no competing interests