This paper presents a multi-robot system for the autonomous detection and treatment of pests. The
system comprises two inspection robots, based on commercial electric vehicles, and two treatment
robots equipped with electric sprayers. All four robots are fitted with cameras and global navigation
satellite system (GNSS) receivers and operate under a cloud-based mission management system.
The robots function decoupled, enabling autonomous navigation, real-time pest detection using
convolutional neural network (CNN) models, and precision treatment application. The system was
validated in Botrytis detection in white grape vineyards, demonstrating accurate path following,
continuous image acquisition with an average 20 % overlap, and 95 % detection accuracy with a
30 % false positive rate., This work has been funded by the EU FlexiGroBots project (https://cordis.europa.eu/project/ id/101017111), Peer reviewed

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems.