Hybrid Method for Rapid Development of Efficient and Robust Models for In-row Crop Segmentation

Paweł Majewski, Jacek Reiner

2022

Abstract

Crop segmentation is a crucial part of computer vision methods for precision agriculture. Two types of crop segmentation approaches can be observed – based on pixel intensity thresholding of vegetation indices and classification-based including context (e.g. deep convolutional neural network). Threshold-based methods work well when images do not contain disruptions (weeds, overlapping, different illumination). Although deep learning methods can cope with the mentioned problems their development requires a large number of labelled samples. In this study, we propose a hybrid method for the rapid development of efficient and robust models for in-row crop segmentation, combining the advantages of described approaches. Our method consists of two-step labelling with the generation of synthetic crop images and the following training of the Mask R-CNN model. The proposed method has been tested comprehensively on samples characterised by different types of disruptions. Already the first labelling step based mainly on cluster labelling significantly increased the average F1-score in crop detection task compared to binary thresholding of vegetation indices. The second stage of the labelling allowed this result to be increased. As part of this research, an algorithm for row detection and row-based filtering was also proposed, which reduced the number of FP errors made during inference.

Paper Citation

in Harvard Style

Majewski P. and Reiner J. (2022). Hybrid Method for Rapid Development of Efficient and Robust Models for In-row Crop Segmentation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 274-281. DOI: 10.5220/0010775400003124

in Bibtex Style

@conference{visapp22,
author={Paweł Majewski and Jacek Reiner},
title={Hybrid Method for Rapid Development of Efficient and Robust Models for In-row Crop Segmentation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={274-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010775400003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Hybrid Method for Rapid Development of Efficient and Robust Models for In-row Crop Segmentation
SN - 978-989-758-555-5
AU - Majewski P.
AU - Reiner J.
PY - 2022
SP - 274
EP - 281
DO - 10.5220/0010775400003124
PB - SciTePress