Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses.

Automation of vineyards cultivation necessitates for mobile robots to retain accurate localization system. The paper introduces a stereo vision-based Graph-Simultaneous Localization and Mapping (Graph-SLAM) pipeline custom-tailored to the specificities of vineyard fields. Graph-SLAM is reinforced with a Loop Closure Detection (LCD) based on semantic segmentation of the vine trees. The Mask R-CNN network is applied to segment the trunk regions of images, on which unique visual features are extracted. These features are used to populate the bag of visual words (BoVW s) retained on the formulated graph. A nearest neighbor search is applied to each query trunk-image to associate each unique feature descriptor with the corresponding node in the graph using a voting procedure. We apply a probabilistic method to select the most suitable loop closing pair and, upon an LCD appearance, the 3D points of the trunks are employed to estimate the loop closure constraint to the graph. The traceable features on trunk segments drastically reduce the number of retained BoVWs, which in turn expedites significantly the loop closure and graph optimization, rendering our method suitable for large scale mapping in vineyards. The pipeline has been evaluated on several data sequences gathered from real vineyards, in different seasons, when the appearance of vine trees vary significantly, and exhibited robust mapping in long distances.

Driven by the increasing food demand and the need for higher-quality cultivation, precision agriculture grows steadily during the last decade. It involves the application of mobile robots and intelligent robotic technologies in various agricultural field tasks, concerning a variety of crop types. Aiming at compensating for the lack of selective robotic harvesting solutions regarding the high-value crop of grapes, the EU-funded project BACCHUS ¹ develops an intelligent mobile robotic system, comprising two independent and cooperative robots: one for the grape inspection and collection of valuable data regarding their maturity level, and one for the bimanual harvesting of grapes in a human-inspired manner. Validated via real-field trials, the proposed autonomous system pushes forward the precision agriculture application for a particularly sensitive crop type in the challenging and heavily cluttered environment of vineyards, facilitating the selective harvesting of high-quality grapes.