Computer Vision based System for Apple Detection in Crops

Mercedes Marzoa Tanco, Gonzalo Tejera, Matías Di Martino

2018

Abstract

In recent times there has been an increasing need to improve apple production competitiveness. The automatic estimation of the crop yield or the automatic collection may contribute to this improvement. This article proposes a simple and efficient approach to automatically detect the apples present on a given set of images. We tested the proposed algorithm on several images taken on many different apple crops under natural lighting conditions. The proposed method has two main steps. First we implement a classification step in which each pixel is classified as part of an apple (positive pixel) or as part of the background (negative pixel). Then, a second step explore the morphology of the set of positive pixels, to detect the most likely configuration of circular structures. We compare the performance of methods such as: Support Vector Machine, k-Nearest Neighbor and a basic Decision Tree on the classification step. A database with 266 high resolution images was created and made publicly available. This database was manually labeled and we provide for each image, a label (positive or negative) for each pixel, plus the location of the center of each apple.

Paper Citation

in Harvard Style

Marzoa Tanco M., Tejera G. and Di Martino M. (2018). Computer Vision based System for Apple Detection in Crops. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 239-249. DOI: 10.5220/0006535002390249

in Bibtex Style

@conference{visapp18,
author={Mercedes Marzoa Tanco and Gonzalo Tejera and Matías Di Martino},
title={Computer Vision based System for Apple Detection in Crops},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={239-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006535002390249},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Computer Vision based System for Apple Detection in Crops
SN - 978-989-758-290-5
AU - Marzoa Tanco M.
AU - Tejera G.
AU - Di Martino M.
PY - 2018
SP - 239
EP - 249
DO - 10.5220/0006535002390249
PB - SciTePress