Real-time Vineyard Trunk Detection for a Grapes Harvesting Robot via Deep Learning

Research and development in agricultural robots are continuously increasing. However, dynamically changing agricultural environments provide adverse conditions to robotics operability. In order to perform the agricultural tasks safely and accurately, reliable landmarks from the surrounding environment need to be identified. In this work, deep learning is employed for accurate and fast detection of high-level features of vineyards, the vine trunks. More specifically, Faster regions-convolutional neural network (Faster R-CNN), You Only Look Once version 3 (YOLOv3) and YOLOv5 are tested for real-time vine trunk detection. The models are trained with an in-house dataset designed for the needs of this study, containing 1927 annotated vine trunks in 899 different images. Comparative results indicate YOLOv5 as the configuration that allows the faster and most accurate vine trunk detection, achieving an overall Average Precision of 73.2% in 29.6 ms. The high precision combined with the fast runtime performance prove that the YOLOv5 detector is suitable for real-time vine trunk detection executed by an autonomous harvesting robot.