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Authors: Carlos Carbone ; Ciro Potena and Daniele Nardi

Affiliation: Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy

ISBN: 978-989-758-444-2

ISSN: 2184-2841

Keyword(s): Unity Engine, Near Infrared, Convolutional Neural Network, Plant-weed Segmentation Classification.

Abstract: Weed spotting through image classification is one of the methods applied in precision agriculture to increase efficiency in crop damage reduction. These classifications are nowadays typically based on deep machine learning with convolutional neural networks (CNN), where a main difficulty is gathering large amounts of labeled data required for the training of these networks. Thus, synthetic dataset sources have been developed including simulations based on graphic engines; however, some data inputs that can improve the performance of CNNs like the near infrared (NIR) have not been considered in these simulations. This paper presents a simulation in the Unity game engine that builds fields of sugar beets with weeds. Images are generated to create datasets that are ready to train CNNs for semantic segmentation. The dataset is tested by comparing classification results from the bonnet CNN network trained with synthetic images and trained with real images, both with RGB and RGBN (RGB+near infrared) as inputs. The preliminary results suggest that the addition of the NIR channel to the simulation for plant-weed segmentation can be effectively exploited. These show a difference of 5.75% for the global mean IoU over 820 classified images by including the NIR data in the unity generated dataset. (More)

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Paper citation in several formats:
Carbone, C.; Potena, C. and Nardi, D. (2020). Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-444-2; ISSN 2184-2841, pages 81-90. DOI: 10.5220/0009827900810090

@conference{simultech20,
author={Carlos Carbone. and Ciro Potena. and Daniele Nardi.},
title={Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification},
booktitle={Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,},
year={2020},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009827900810090},
isbn={978-989-758-444-2},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,
TI - Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification
SN - 978-989-758-444-2
IS - 2184-2841
AU - Carbone, C.
AU - Potena, C.
AU - Nardi, D.
PY - 2020
SP - 81
EP - 90
DO - 10.5220/0009827900810090

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