Detecting Overgrown Plant Species Occluding Other Species in Complex Vegetation in Agricultural Fields Based on Temporal Changes in RGB Images and Deep Learning

Haruka Ide, Hiroyuki Ogata, Takuya Otani, Atsuo Takanishi, Jun Ohya

2024

Abstract

Synecoculture cultivates useful plants while expanding biodiversity in farmland, but the complexity of its management requires the establishment of new automated systems for management. In particular, pruning overgrown dominant species that lead to reduced diversity is an important task. This paper proposes a method for detecting overgrown plant species occluding other species from the camera fixed in a Synecoculture farm. The camera acquires time series images once a week soon after seeding. Then, a deep learning based semantic segmentation is applied to each of the weekly images. The plant species map, which consist of multiple layers corresponding to the segmented species, is created by storing the number of the existence of that plant species over weeks at each pixel in that layer. Finally, we combine the semantic segmentation results with the earlier plant species map so that occluding overgrown species and occluded species are detected. As a result of conducting experiments using six sets of time series images acquired over six weeks, (1) UNet-Resnet101 is most accurate for semantic segmentation, (2) Using both segmentation and plant species map achieves significantly higher segmentation accuracies than without plant species map, (3) Overgrown, occluding species and occluded species are successfully detected.

Paper Citation

in Harvard Style

Ide H., Ogata H., Otani T., Takanishi A. and Ohya J. (2024). Detecting Overgrown Plant Species Occluding Other Species in Complex Vegetation in Agricultural Fields Based on Temporal Changes in RGB Images and Deep Learning. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 266-273. DOI: 10.5220/0012352500003654

in Bibtex Style

@conference{icpram24,
author={Haruka Ide and Hiroyuki Ogata and Takuya Otani and Atsuo Takanishi and Jun Ohya},
title={Detecting Overgrown Plant Species Occluding Other Species in Complex Vegetation in Agricultural Fields Based on Temporal Changes in RGB Images and Deep Learning},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={266-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012352500003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Detecting Overgrown Plant Species Occluding Other Species in Complex Vegetation in Agricultural Fields Based on Temporal Changes in RGB Images and Deep Learning
SN - 978-989-758-684-2
AU - Ide H.
AU - Ogata H.
AU - Otani T.
AU - Takanishi A.
AU - Ohya J.
PY - 2024
SP - 266
EP - 273
DO - 10.5220/0012352500003654
PB - SciTePress