Gianmarco Roggiolani — accepted AI-conference papers

2023

Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain

Gianmarco Roggiolani, Matteo Sodano, Tiziano Guadagnino, Federico Magistri, Jens Behley, Cyrill Stachniss

ICRA 2023poster

Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the…

Cited by 37 Source ↗code ↗Scholar ↗

2023

On Domain-Specific Pre- Training for Effective Semantic Perception in Agricultural Robotics

Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino, Jan Weyler, Giorgio Grisetti, Cyrill Stachniss +1

ICRA 2023poster

Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic…

Cited by 5 Source ↗Scholar ↗

2023

Unsupervised Pre-Training for 3D Leaf Instance Segmentation

Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino, Jens Behley, Cyrill Stachniss

RA-L 2023

Crops for food, feed, fiber, and fuel are key natural resources for our society. Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping. Traditionally, this task is done manually, which is time- and labor-intensive. Robots can automate

Cited by 8 Source ↗Scholar ↗