Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0.

Clinical and haemodynamic effects of percutaneous edge-to-edge mitral valve repair in atrial versus ventricular functional mitral regurgitation

Background

Atrial functional mitral regurgitation (A-FMR) is a novel entity characterized by a MR due to atrial remodeling but with preserved left ventricular (LV) systolic function.

Purpose

To assess the clinical and haemodynamic impact of percutaneous edge-to-edge mitral valve repair with MitraClip in patients with A-FMR as compared to ventricular (V)-FMR.

Methods

MR grade, functional status (NYHA class), and major adverse cardiac events (MACE= all-cause mortality or hospitalization for heart failure (HF)) were evaluated in 52 A-FMR patients (pts.) and in 307 V-FMR pts. who underwent MitraClip implantation in 7 Belgian centers. In a subgroup of 56 pts (10 A-FMR and 46 V-FMR) haemodynamic assessment during a symptom-limited exercise echocardiography was performed before and 6-month after intervention.

Results

MitraClip implantation resulted in similar MR reductions in A-FMR and V-FMR (MR grade ≤2 at 6-month in 94% versus 82%, respectively (p=0.08)) and was associated with improvement of functional status in both groups (NYHA class ≤2 at 6 months in 90% versus 80%, respectively (p=0.2)). Serial haemodynamic assessment revealed that the cardiac output at 6-month was significantly higher in A-FMR pts. both at rest (5.1±1.5 L/min versus 3.8±1.5 L/min, p=0.002) and during peak exercise (7.9±2.4 L/min versus 6.1±2.1 L/min, p=0.02). Also the reduction in systolic pulmonary artery pressure (sPAP) was more pronounced in A-FMR: Δ sPAP at rest – 13.1±15.1 mmHg versus – 2.2±13.3 mmHg (p=0.03). During a follow-up period of 1.3±1.2 years MACE rate was significantly lower in A-FMR versus V-FMR with an adjusted OR of 0.46 (95% CI 0.24–0.88, see figure), which was mainly driven by a reduction in HF hospitalization.

Conclusion

Percutaneous edge-to-edge mitral valve repair with MitraClip is at least as effective in A-FMR as in V-FMR in reducing MR. But, the haemodynamic and clinical impact is stronger in A-FMR pts.

Funding Acknowledgement

Type of funding sources: None. MACE in A-FMR versus V-FMR pts