most citedIn-field high throughput grapevine phenotyping with a consumer-grade depth camera

145 citations · 425 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2021145 cited

In-field high throughput grapevine phenotyping with a consumer-grade depth camera

Annalisa Milella, Roberto Marani, Antonio Petitti +1

Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective cr…

cs.RO202167 cited

Terrain assessment for precision agriculture using vehicle dynamic modelling

Giulio Reina, Annalisa Milella, Rocco Galati

Advances in precision agriculture greatly rely on innovative control and sensing technologies that allow service units to increase their level of driving automation while ensuring…

cs.RO2021125 cited

Ambient awareness for agricultural robotic vehicles

Giulio Reina, Annalisa Milella, Raphael Rouveure +3

In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate…

cs.RO202167 cited

A multi-sensor robotic platform for ground mapping and estimation beyond the visible spectrum

Annalisa Milella, Giulio Reina, Michael Nielsen

Accurate soil mapping is critical for a highly-automated agricultural vehicle to successfully accomplish important tasks including seeding, ploughing, fertilising and controlled tr…

eess.SY201921 cited

Mind the ground: A Power Spectral Density-based estimator for all-terrain rovers

Giulio Reina, Antonio Leanza, Annalisa Milella +1

There is a growing interest in new sensing technologies and processing algorithms to increase the level of driving automation towards self-driving vehicles. The challenge for auton…