Integration of AI-Based Weed Detection and Robotic Actuation for Site-Specific Under-Canopy Spraying in Woody Crops

Weed management in woody perennial crops relies mainly on broadcast herbicide application, with well-documented costs to soil health, biodiversity, and crop physiology. Robotic platforms offer a path toward selective, site-specific control, but orchard environments present challenges such as irregular geometries, trunks, and strong illumination variability under the canopy that have limited fully integrated solutions. This work presents an autonomous robotic platform for selective under-canopy weed control in woody crops, combining multi-sensor perception, a six-degree-of-freedom robotic arm with a mechanical trunk-avoidance mechanism, and a precision spraying module with independently controlled nozzles. A weed image dataset tailored to Mediterranean orchard conditions was built from controlled-cultivation and commercial-orchard imagery under a two-phase training strategy, and the platform was evaluated in a commercial almond orchard in southern Spain. Field trials confirmed the platform’s ability to avoid tree trunks (presenting an average of 1.28% coverage near the tree trunks) and spray only selected targets under typical orchard operation but weed detection accuracy dropped substantially between winter conditions (mAP@0.5 = 93.5%) and summer conditions (mAP@0.5 = 47.8%), with uneven canopy lighting identified as the main cause. These results confirm the technical feasibility of integrating perception, navigation, and actuation into a single autonomous platform, while highlighting robust perception under canopy-induced illumination heterogeneity and tighter perception–navigation integration as the main remaining challenges.