Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems.

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.