This work presents an open-source simulation framework designed to extend the capabilities of the VRX environment for developing and validating control strategies for surface robotic vehicles. The platform features a custom monohull, kayak-type USV with four thrusters in differential configuration, represented with a complete graphical mockup consistent with its physical design and modeled with realistic dynamics and sensor integration. A thrust mapping function was calibrated using manufacturer data, and the vehicle’s behavior was characterized using a simplified Fossen model with parameters identified via Least Squares estimation. Multiple motion controllers, including velocity, position, trajectory tracking, and path guidance, were implemented and evaluated in a variety of wave and wind scenarios designed to test the full vehicle dynamics and closed-loop behavior. In addition to extending the VRX simulator, this work introduces a new USV model, a calibrated thrust response, and a set of model-based controllers validated in high-fidelity marine conditions. The resulting framework constitutes a reproducible and extensible resource for the marine robotics community, with direct applications in robotic education, perception, and advanced control systems.

Early pest detection in edible crops demands sensing solutions that can run at the edge under tight power, budget, and maintenance constraints. This review synthesizes peer-reviewed work (2015–2025) on three modality families—vision/AI, spectroscopy/imaging spectroscopy, and indirect sensors—restricted to edible crops and studies reporting some implementation or testing (n = 178; IEEE Xplore and Scopus). Each article was scored with a modality-aware performance–cost–implementability (PCI) rubric using category-specific weights, and the inter-reviewer reliability was quantified with weighted Cohen’s $\kappa$. We translated the evidence into compact decision maps for common deployment profiles (low-power rapid rollout; high-accuracy cost-flexible; and block-scale scouting). Across the corpus, vision/AI and well-engineered sensor systems more often reached deployment-leaning PCI (≥3.5: 32.0% and 33.3%, respectively) than spectroscopy (18.2%); the median PCI was 3.20 (AI), 3.17 (sensors), and 2.60 (spectroscopy). A Pareto analysis highlighted detector/attention models near $(P,C,I)\approx (4,5,4)$; sensor nodes spanning balanced $(4,4,4)$ and ultra-lean $(2,5,4)$ trade-offs; and the spectroscopy split between the early-warning strength $(5,4,3)$ and portability $(4,3,4)$. The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic $\kappa$ = 0.73–0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: ${\kappa }_{\mathrm{quadratic}}=0.30$–$0.44$), supporting rubric stability with adjacency-dominated disagreements. The decision maps operationalize these findings, helping practitioners select a fit-for-purpose modality and encouraging a minimum PCI metadata set to enable reproducible, deployment-oriented comparisons.