Automatic Counting of Wheat Spikes from Wheat Growth Images

Najmah Alharbi, Ji Zhou, Wenija Wang

Abstract

This study aims to develop an automated screening system that can estimate the number of wheat spikes (i.e. ears) from a given wheat plant image acquired after the flowering stage. The platform can be used to assist the dynamic estimation of wheat yield potential as well as grain yield based on wheat images captured by the CropQuant platform. Our proposed system framework comprises three main stages. Firstly, it transforms the wheat plant raw image data using colour index of vegetation extraction (CIVE) and then segments wheat ear regions from the image to reduce the influence of the background signals. Secondly, it detects wheat ears using Gabor filter banks and K-means clustering algorithm. Finally, it estimates the number of wheat spikes within extracted wheat spike region through a regression method. The framework is tested with a real-world dataset of wheat growth images equally distributed from flowering to ripening stages. The estimations of the wheat ears were benchmarked against the ground truth produced in this study by human manual counting. Our automatic counting system achieved an average accuracy of 90.7% with a standard deviation of 0.055, at a much faster speed than human experts and hence the system has a potential to be improved for agricultural applications on wheat growth studies in the future.

References

Download


Paper Citation


in Harvard Style

Alharbi N., Zhou J. and Wang W. (2018). Automatic Counting of Wheat Spikes from Wheat Growth Images.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 346-355. DOI: 10.5220/0006580403460355


in Bibtex Style

@conference{icpram18,
author={Najmah Alharbi and Ji Zhou and Wenija Wang},
title={Automatic Counting of Wheat Spikes from Wheat Growth Images},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={346-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006580403460355},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Automatic Counting of Wheat Spikes from Wheat Growth Images
SN - 978-989-758-276-9
AU - Alharbi N.
AU - Zhou J.
AU - Wang W.
PY - 2018
SP - 346
EP - 355
DO - 10.5220/0006580403460355