Vinymap: a Vineyard Inspection and 3D Reconstruction Framework for Agricultural Robots

Efficient and thorough vineyard inspection is crucial for optimizing yield and preventing disease from spreading. Manual approaches are labor-intensive and prone to human error, motivating the development of automated solutions. Precision viticulture benefits greatly from access to photo-realistic 3D vineyard maps and from capturing intricate visual details necessary for accurate canopy and grape health assessment. Generating such maps efficiently proves challenging, particularly when employing cost-effective equipment. This paper presents a novel vineyard inspection and 3D reconstruction framework implemented on a Robotic Platform (RP) equipped with three stereo cameras. The framework’s performance was evaluated on an experimental synthetic vineyard developed at NTUA. This testing setup allowed experimentation under diverse lighting conditions, ensuring the system’s robustness under realistic scenarios. Unlike existing solutions, which often focus on specific aspects of the inspection, our framework offers a top-down approach, encompassing autonomous navigation, high-fidelity 3D reconstruction, and canopy growth assessment. The developed software is available at the Control Systems Laboratory’s (CSL) bitbucket repository [1].